Analysis of Occupational Accidents in Underground and Surface Mining in Spain Using Data-Mining Techniques

PubMed Central

Sanmiquel, Lluís; Bascompta, Marc; Rossell, Josep M.; Anticoi, Hernán Francisco; Guash, Eduard

2018-01-01

An analysis of occupational accidents in the mining sector was conducted using the data from the Spanish Ministry of Employment and Social Safety between 2005 and 2015, and data-mining techniques were applied. Data was processed with the software Weka. Two scenarios were chosen from the accidents database: surface and underground mining. The most important variables involved in occupational accidents and their association rules were determined. These rules are composed of several predictor variables that cause accidents, defining its characteristics and context. This study exposes the 20 most important association rules in the sector—either surface or underground mining—based on the statistical confidence levels of each rule as obtained by Weka. The outcomes display the most typical immediate causes, along with the percentage of accidents with a basis in each association rule. The most important immediate cause is body movement with physical effort or overexertion, and the type of accident is physical effort or overexertion. On the other hand, the second most important immediate cause and type of accident are different between the two scenarios. Data-mining techniques were chosen as a useful tool to find out the root cause of the accidents. PMID:29518921

Revealing Significant Relations between Chemical/Biological Features and Activity: Associative Classification Mining for Drug Discovery

ERIC Educational Resources Information Center

Yu, Pulan

2012-01-01

Classification, clustering and association mining are major tasks of data mining and have been widely used for knowledge discovery. Associative classification mining, the combination of both association rule mining and classification, has emerged as an indispensable way to support decision making and scientific research. In particular, it offers a…

An Incremental High-Utility Mining Algorithm with Transaction Insertion

PubMed Central

Gan, Wensheng; Zhang, Binbin

2015-01-01

Association-rule mining is commonly used to discover useful and meaningful patterns from a very large database. It only considers the occurrence frequencies of items to reveal the relationships among itemsets. Traditional association-rule mining is, however, not suitable in real-world applications since the purchased items from a customer may have various factors, such as profit or quantity. High-utility mining was designed to solve the limitations of association-rule mining by considering both the quantity and profit measures. Most algorithms of high-utility mining are designed to handle the static database. Fewer researches handle the dynamic high-utility mining with transaction insertion, thus requiring the computations of database rescan and combination explosion of pattern-growth mechanism. In this paper, an efficient incremental algorithm with transaction insertion is designed to reduce computations without candidate generation based on the utility-list structures. The enumeration tree and the relationships between 2-itemsets are also adopted in the proposed algorithm to speed up the computations. Several experiments are conducted to show the performance of the proposed algorithm in terms of runtime, memory consumption, and number of generated patterns. PMID:25811038

Mining Rare Associations between Biological Ontologies

PubMed Central

Benites, Fernando; Simon, Svenja; Sapozhnikova, Elena

2014-01-01

The constantly increasing volume and complexity of available biological data requires new methods for their management and analysis. An important challenge is the integration of information from different sources in order to discover possible hidden relations between already known data. In this paper we introduce a data mining approach which relates biological ontologies by mining cross and intra-ontology pairwise generalized association rules. Its advantage is sensitivity to rare associations, for these are important for biologists. We propose a new class of interestingness measures designed for hierarchically organized rules. These measures allow one to select the most important rules and to take into account rare cases. They favor rules with an actual interestingness value that exceeds the expected value. The latter is calculated taking into account the parent rule. We demonstrate this approach by applying it to the analysis of data from Gene Ontology and GPCR databases. Our objective is to discover interesting relations between two different ontologies or parts of a single ontology. The association rules that are thus discovered can provide the user with new knowledge about underlying biological processes or help improve annotation consistency. The obtained results show that produced rules represent meaningful and quite reliable associations. PMID:24404165

Mining rare associations between biological ontologies.

PubMed

Benites, Fernando; Simon, Svenja; Sapozhnikova, Elena

2014-01-01

The constantly increasing volume and complexity of available biological data requires new methods for their management and analysis. An important challenge is the integration of information from different sources in order to discover possible hidden relations between already known data. In this paper we introduce a data mining approach which relates biological ontologies by mining cross and intra-ontology pairwise generalized association rules. Its advantage is sensitivity to rare associations, for these are important for biologists. We propose a new class of interestingness measures designed for hierarchically organized rules. These measures allow one to select the most important rules and to take into account rare cases. They favor rules with an actual interestingness value that exceeds the expected value. The latter is calculated taking into account the parent rule. We demonstrate this approach by applying it to the analysis of data from Gene Ontology and GPCR databases. Our objective is to discover interesting relations between two different ontologies or parts of a single ontology. The association rules that are thus discovered can provide the user with new knowledge about underlying biological processes or help improve annotation consistency. The obtained results show that produced rules represent meaningful and quite reliable associations.

The association rules search of Indonesian university graduate’s data using FP-growth algorithm

NASA Astrophysics Data System (ADS)

Faza, S.; Rahmat, R. F.; Nababan, E. B.; Arisandi, D.; Effendi, S.

2018-02-01

The attribute varieties in university graduates data have caused frustrations to the institution in finding the combinations of attributes that often emerge and have high integration between attributes. Association rules mining is a data mining technique to determine the integration of the data or the way of a data set affects another set of data. By way of explanation, there are possibilities in finding the integration of data on a large scale. Frequent Pattern-Growth (FP-Growth) algorithm is one of the association rules mining technique to determine a frequent itemset in an FP-Tree data set. From the research on the search of university graduate’s association rules, it can be concluded that the most common attributes that have high integration between them are in the combination of State-owned High School outside Medan, regular university entrance exam, GPA of 3.00 to 3.49 and over 4-year-long study duration.

Big data mining analysis method based on cloud computing

NASA Astrophysics Data System (ADS)

Cai, Qing Qiu; Cui, Hong Gang; Tang, Hao

2017-08-01

Information explosion era, large data super-large, discrete and non-(semi) structured features have gone far beyond the traditional data management can carry the scope of the way. With the arrival of the cloud computing era, cloud computing provides a new technical way to analyze the massive data mining, which can effectively solve the problem that the traditional data mining method cannot adapt to massive data mining. This paper introduces the meaning and characteristics of cloud computing, analyzes the advantages of using cloud computing technology to realize data mining, designs the mining algorithm of association rules based on MapReduce parallel processing architecture, and carries out the experimental verification. The algorithm of parallel association rule mining based on cloud computing platform can greatly improve the execution speed of data mining.

Extracting Cross-Ontology Weighted Association Rules from Gene Ontology Annotations.

PubMed

Agapito, Giuseppe; Milano, Marianna; Guzzi, Pietro Hiram; Cannataro, Mario

2016-01-01

Gene Ontology (GO) is a structured repository of concepts (GO Terms) that are associated to one or more gene products through a process referred to as annotation. The analysis of annotated data is an important opportunity for bioinformatics. There are different approaches of analysis, among those, the use of association rules (AR) which provides useful knowledge, discovering biologically relevant associations between terms of GO, not previously known. In a previous work, we introduced GO-WAR (Gene Ontology-based Weighted Association Rules), a methodology for extracting weighted association rules from ontology-based annotated datasets. We here adapt the GO-WAR algorithm to mine cross-ontology association rules, i.e., rules that involve GO terms present in the three sub-ontologies of GO. We conduct a deep performance evaluation of GO-WAR by mining publicly available GO annotated datasets, showing how GO-WAR outperforms current state of the art approaches.

An Algorithm of Association Rule Mining for Microbial Energy Prospection

PubMed Central

Shaheen, Muhammad; Shahbaz, Muhammad

2017-01-01

The presence of hydrocarbons beneath earth’s surface produces some microbiological anomalies in soils and sediments. The detection of such microbial populations involves pure bio chemical processes which are specialized, expensive and time consuming. This paper proposes a new algorithm of context based association rule mining on non spatial data. The algorithm is a modified form of already developed algorithm which was for spatial database only. The algorithm is applied to mine context based association rules on microbial database to extract interesting and useful associations of microbial attributes with existence of hydrocarbon reserve. The surface and soil manifestations caused by the presence of hydrocarbon oxidizing microbes are selected from existing literature and stored in a shared database. The algorithm is applied on the said database to generate direct and indirect associations among the stored microbial indicators. These associations are then correlated with the probability of hydrocarbon’s existence. The numerical evaluation shows better accuracy for non-spatial data as compared to conventional algorithms at generating reliable and robust rules. PMID:28393846

Soil quality assessment using weighted fuzzy association rules

USGS Publications Warehouse

Xue, Yue-Ju; Liu, Shu-Guang; Hu, Yue-Ming; Yang, Jing-Feng

2010-01-01

Fuzzy association rules (FARs) can be powerful in assessing regional soil quality, a critical step prior to land planning and utilization; however, traditional FARs mined from soil quality database, ignoring the importance variability of the rules, can be redundant and far from optimal. In this study, we developed a method applying different weights to traditional FARs to improve accuracy of soil quality assessment. After the FARs for soil quality assessment were mined, redundant rules were eliminated according to whether the rules were significant or not in reducing the complexity of the soil quality assessment models and in improving the comprehensibility of FARs. The global weights, each representing the importance of a FAR in soil quality assessment, were then introduced and refined using a gradient descent optimization method. This method was applied to the assessment of soil resources conditions in Guangdong Province, China. The new approach had an accuracy of 87%, when 15 rules were mined, as compared with 76% from the traditional approach. The accuracy increased to 96% when 32 rules were mined, in contrast to 88% from the traditional approach. These results demonstrated an improved comprehensibility of FARs and a high accuracy of the proposed method.

Real-time intelligent decision making with data mining

NASA Astrophysics Data System (ADS)

Gupta, Deepak P.; Gopalakrishnan, Bhaskaran

2004-03-01

Database mining, widely known as knowledge discovery and data mining (KDD), has attracted lot of attention in recent years. With the rapid growth of databases in commercial, industrial, administrative and other applications, it is necessary and interesting to extract knowledge automatically from huge amount of data. Almost all the organizations are generating data and information at an unprecedented rate and they need to get some useful information from this data. Data mining is the extraction of non-trivial, previously unknown and potentially useful patterns, trends, dependence and correlation known as association rules among data values in large databases. In last ten to fifteen years, data mining spread out from one company to the other to help them understand more about customers' aspect of quality and response and also distinguish the customers they want from those they do not. A credit-card company found that customers who complete their applications in pencil rather than pen are more likely to default. There is a program that identifies callers by purchase history. The bigger the spender, the quicker the call will be answered. If you feel your call is being answered in the order in which it was received, think again. Many algorithms assume that data is static in nature and mine the rules and relations in that data. But for a dynamic database e.g. in most of the manufacturing industries, the rules and relations thus developed among the variables/items no longer hold true. A simple approach may be to mine the associations among the variables after every fixed period of time. But again, how much the length of this period should be, is a question to be answered. The next problem with the static data mining is that some of the relationships that might be of interest from one period to the other may be lost after a new set of data is used. To reflect the effect of new data set and current status of the association rules where some of the strong rules might become weak and vice versa, there is a need to develop an efficient algorithm to adapt to the current patterns and associations. Some work has been done in developing the association rules for incremental database but to the best of the author"s knowledge no work has been done to do the same for periodic cause and effect analysis for online association rules in manufacturing industries. The present research attempts to answer these questions and develop an algorithm that can display the association rules online, find the periodic patterns in the data and detect the root cause of the problem.

Effective Diagnosis of Alzheimer's Disease by Means of Association Rules

NASA Astrophysics Data System (ADS)

Chaves, R.; Ramírez, J.; Górriz, J. M.; López, M.; Salas-Gonzalez, D.; Illán, I.; Segovia, F.; Padilla, P.

In this paper we present a novel classification method of SPECT images for the early diagnosis of the Alzheimer's disease (AD). The proposed method is based on Association Rules (ARs) aiming to discover interesting associations between attributes contained in the database. The system uses firstly voxel-as-features (VAF) and Activation Estimation (AE) to find tridimensional activated brain regions of interest (ROIs) for each patient. These ROIs act as inputs to secondly mining ARs between activated blocks for controls, with a specified minimum support and minimum confidence. ARs are mined in supervised mode, using information previously extracted from the most discriminant rules for centering interest in the relevant brain areas, reducing the computational requirement of the system. Finally classification process is performed depending on the number of previously mined rules verified by each subject, yielding an up to 95.87% classification accuracy, thus outperforming recent developed methods for AD diagnosis.

The application of data mining to explore association rules between metabolic syndrome and lifestyles.

PubMed

Huang, Yi Chao

This study used an efficient data mining algorithm, called DCIP (the data cutting and inner product method), to explore association rules between the lifestyles of factory workers in Taiwan and the metabolic syndrome. A total of 1,216 workers in four companies completed a lifestyle questionnaire. Results of the questionnaire survey were integrated into the workers' health examination reports to form an attribute database of the metabolic syndrome. Among the association rules derived by DCIP, 80% of those on the list of the top 15 highest support counts are corroborated by medical literature or by healthcare professionals. These findings prove that data mining is a valid and effective research method, and that larger sample sizes will likely produce more accurate associations connecting the metabolic syndrome to specific lifestyles. The rules already verified can serve as a reference guide for the health management of factory workers. The remaining 20%, while still lacking hard evidence, provide fertile ground for future research.

Predicting missing values in a home care database using an adaptive uncertainty rule method.

PubMed

Konias, S; Gogou, G; Bamidis, P D; Vlahavas, I; Maglaveras, N

2005-01-01

Contemporary literature illustrates an abundance of adaptive algorithms for mining association rules. However, most literature is unable to deal with the peculiarities, such as missing values and dynamic data creation, that are frequently encountered in fields like medicine. This paper proposes an uncertainty rule method that uses an adaptive threshold for filling missing values in newly added records. A new approach for mining uncertainty rules and filling missing values is proposed, which is in turn particularly suitable for dynamic databases, like the ones used in home care systems. In this study, a new data mining method named FiMV (Filling Missing Values) is illustrated based on the mined uncertainty rules. Uncertainty rules have quite a similar structure to association rules and are extracted by an algorithm proposed in previous work, namely AURG (Adaptive Uncertainty Rule Generation). The main target was to implement an appropriate method for recovering missing values in a dynamic database, where new records are continuously added, without needing to specify any kind of thresholds beforehand. The method was applied to a home care monitoring system database. Randomly, multiple missing values for each record's attributes (rate 5-20% by 5% increments) were introduced in the initial dataset. FiMV demonstrated 100% completion rates with over 90% success in each case, while usual approaches, where all records with missing values are ignored or thresholds are required, experienced significantly reduced completion and success rates. It is concluded that the proposed method is appropriate for the data-cleaning step of the Knowledge Discovery process in databases. The latter, containing much significance for the output efficiency of any data mining technique, can improve the quality of the mined information.

Empirical evaluation of interest-level criteria

NASA Astrophysics Data System (ADS)

Sahar, Sigal; Mansour, Yishay

1999-02-01

Efficient association rule mining algorithms already exist, however, as the size of databases increases, the number of patterns mined by the algorithms increases to such an extent that their manual evaluation becomes impractical. Automatic evaluation methods are, therefore, required in order to sift through the initial list of rules, which the datamining algorithm outputs. These evaluation methods, or criteria, rank the association rules mined from the dataset. We empirically examined several such statistical criteria: new criteria, as well as previously known ones. The empirical evaluation was conducted using several databases, including a large real-life dataset, acquired from an order-by-phone grocery store, a dataset composed from www proxy logs, and several datasets from the UCI repository. We were interested in discovering whether the ranking performed by the various criteria is similar or easily distinguishable. Our evaluation detected, when significant differences exist, three patterns of behavior in the eight criteria we examined. There is an obvious dilemma in determining how many association rules to choose (in accordance with support and confidence parameters). The tradeoff is between having stringent parameters and, therefore, few rules, or lenient parameters and, thus, a multitude of rules. In many cases, our empirical evaluation revealed that most of the rules found by the comparably strict parameters ranked highly according to the interestingness criteria, when using lax parameters (producing significantly more association rules). Finally, we discuss the association rules that ranked highest, explain why these results are sound, and how they direct future research.

Intertransaction Class Association Rule Mining Based on Genetic Network Programming and Its Application to Stock Market Prediction

NASA Astrophysics Data System (ADS)

Yang, Yuchen; Mabu, Shingo; Shimada, Kaoru; Hirasawa, Kotaro

Intertransaction association rules have been reported to be useful in many fields such as stock market prediction, but still there are not so many efficient methods to dig them out from large data sets. Furthermore, how to use and measure these more complex rules should be considered carefully. In this paper, we propose a new intertransaction class association rule mining method based on Genetic Network Programming (GNP), which has the ability to overcome some shortages of Apriori-like based intertransaction association methods. Moreover, a general classifier model for intertransaction rules is also introduced. In experiments on the real world application of stock market prediction, the method shows its efficiency and ability to obtain good results and can bring more benefits with a suitable classifier considering larger interval span.

Promoter Sequences Prediction Using Relational Association Rule Mining

PubMed Central

Czibula, Gabriela; Bocicor, Maria-Iuliana; Czibula, Istvan Gergely

2012-01-01

In this paper we are approaching, from a computational perspective, the problem of promoter sequences prediction, an important problem within the field of bioinformatics. As the conditions for a DNA sequence to function as a promoter are not known, machine learning based classification models are still developed to approach the problem of promoter identification in the DNA. We are proposing a classification model based on relational association rules mining. Relational association rules are a particular type of association rules and describe numerical orderings between attributes that commonly occur over a data set. Our classifier is based on the discovery of relational association rules for predicting if a DNA sequence contains or not a promoter region. An experimental evaluation of the proposed model and comparison with similar existing approaches is provided. The obtained results show that our classifier overperforms the existing techniques for identifying promoter sequences, confirming the potential of our proposal. PMID:22563233

Techniques of Acceleration for Association Rule Induction with Pseudo Artificial Life Algorithm

NASA Astrophysics Data System (ADS)

Kanakubo, Masaaki; Hagiwara, Masafumi

Frequent patterns mining is one of the important problems in data mining. Generally, the number of potential rules grows rapidly as the size of database increases. It is therefore hard for a user to extract the association rules. To avoid such a difficulty, we propose a new method for association rule induction with pseudo artificial life approach. The proposed method is to decide whether there exists an item set which contains N or more items in two transactions. If it exists, a series of item sets which are contained in the part of transactions will be recorded. The iteration of this step contributes to the extraction of association rules. It is not necessary to calculate the huge number of candidate rules. In the evaluation test, we compared the extracted association rules using our method with the rules using other algorithms like Apriori algorithm. As a result of the evaluation using huge retail market basket data, our method is approximately 10 and 20 times faster than the Apriori algorithm and many its variants.

Privacy Preserving Association Rule Mining Revisited: Privacy Enhancement and Resources Efficiency

NASA Astrophysics Data System (ADS)

Mohaisen, Abedelaziz; Jho, Nam-Su; Hong, Dowon; Nyang, Daehun

Privacy preserving association rule mining algorithms have been designed for discovering the relations between variables in data while maintaining the data privacy. In this article we revise one of the recently introduced schemes for association rule mining using fake transactions (FS). In particular, our analysis shows that the FS scheme has exhaustive storage and high computation requirements for guaranteeing a reasonable level of privacy. We introduce a realistic definition of privacy that benefits from the average case privacy and motivates the study of a weakness in the structure of FS by fake transactions filtering. In order to overcome this problem, we improve the FS scheme by presenting a hybrid scheme that considers both privacy and resources as two concurrent guidelines. Analytical and empirical results show the efficiency and applicability of our proposed scheme.

Collaborative Data Mining Tool for Education

ERIC Educational Resources Information Center

Garcia, Enrique; Romero, Cristobal; Ventura, Sebastian; Gea, Miguel; de Castro, Carlos

2009-01-01

This paper describes a collaborative educational data mining tool based on association rule mining for the continuous improvement of e-learning courses allowing teachers with similar course's profile sharing and scoring the discovered information. This mining tool is oriented to be used by instructors non experts in data mining such that, its…

A novel approach for incremental uncertainty rule generation from databases with missing values handling: application to dynamic medical databases.

PubMed

Konias, Sokratis; Chouvarda, Ioanna; Vlahavas, Ioannis; Maglaveras, Nicos

2005-09-01

Current approaches for mining association rules usually assume that the mining is performed in a static database, where the problem of missing attribute values does not practically exist. However, these assumptions are not preserved in some medical databases, like in a home care system. In this paper, a novel uncertainty rule algorithm is illustrated, namely URG-2 (Uncertainty Rule Generator), which addresses the problem of mining dynamic databases containing missing values. This algorithm requires only one pass from the initial dataset in order to generate the item set, while new metrics corresponding to the notion of Support and Confidence are used. URG-2 was evaluated over two medical databases, introducing randomly multiple missing values for each record's attribute (rate: 5-20% by 5% increments) in the initial dataset. Compared with the classical approach (records with missing values are ignored), the proposed algorithm was more robust in mining rules from datasets containing missing values. In all cases, the difference in preserving the initial rules ranged between 30% and 60% in favour of URG-2. Moreover, due to its incremental nature, URG-2 saved over 90% of the time required for thorough re-mining. Thus, the proposed algorithm can offer a preferable solution for mining in dynamic relational databases.

Mining Hesitation Information by Vague Association Rules

NASA Astrophysics Data System (ADS)

Lu, An; Ng, Wilfred

In many online shopping applications, such as Amazon and eBay, traditional Association Rule (AR) mining has limitations as it only deals with the items that are sold but ignores the items that are almost sold (for example, those items that are put into the basket but not checked out). We say that those almost sold items carry hesitation information, since customers are hesitating to buy them. The hesitation information of items is valuable knowledge for the design of good selling strategies. However, there is no conceptual model that is able to capture different statuses of hesitation information. Herein, we apply and extend vague set theory in the context of AR mining. We define the concepts of attractiveness and hesitation of an item, which represent the overall information of a customer's intent on an item. Based on the two concepts, we propose the notion of Vague Association Rules (VARs). We devise an efficient algorithm to mine the VARs. Our experiments show that our algorithm is efficient and the VARs capture more specific and richer information than do the traditional ARs.

Association Rule Mining from an Intelligent Tutor

ERIC Educational Resources Information Center

Dogan, Buket; Camurcu, A. Yilmaz

2008-01-01

Educational data mining is a very novel research area, offering fertile ground for many interesting data mining applications. Educational data mining can extract useful information from educational activities for better understanding and assessment of the student learning process. In this way, it is possible to explore how students learn topics in…

Software tool for data mining and its applications

NASA Astrophysics Data System (ADS)

Yang, Jie; Ye, Chenzhou; Chen, Nianyi

2002-03-01

A software tool for data mining is introduced, which integrates pattern recognition (PCA, Fisher, clustering, hyperenvelop, regression), artificial intelligence (knowledge representation, decision trees), statistical learning (rough set, support vector machine), computational intelligence (neural network, genetic algorithm, fuzzy systems). It consists of nine function models: pattern recognition, decision trees, association rule, fuzzy rule, neural network, genetic algorithm, Hyper Envelop, support vector machine, visualization. The principle and knowledge representation of some function models of data mining are described. The software tool of data mining is realized by Visual C++ under Windows 2000. Nonmonotony in data mining is dealt with by concept hierarchy and layered mining. The software tool of data mining has satisfactorily applied in the prediction of regularities of the formation of ternary intermetallic compounds in alloy systems, and diagnosis of brain glioma.

Java implementation of Class Association Rule algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tamura, Makio

2007-08-30

Java implementation of three Class Association Rule mining algorithms, NETCAR, CARapriori, and clustering based rule mining. NETCAR algorithm is a novel algorithm developed by Makio Tamura. The algorithm is discussed in a paper: UCRL-JRNL-232466-DRAFT, and would be published in a peer review scientific journal. The software is used to extract combinations of genes relevant with a phenotype from a phylogenetic profile and a phenotype profile. The phylogenetic profiles is represented by a binary matrix and a phenotype profile is represented by a binary vector. The present application of this software will be in genome analysis, however, it could be appliedmore » more generally.« less

Quantifying Associations between Environmental Stressors and Demographic Factors

EPA Science Inventory

Association rule mining (ARM) [1-3], also known as frequent item set mining [4] or market basket analysis [1], has been widely applied in many different areas, such as business product portfolio planning [5], intrusion detection infrastructure design [6], gene expression analysis...

Power System Transient Stability Based on Data Mining Theory

NASA Astrophysics Data System (ADS)

Cui, Zhen; Shi, Jia; Wu, Runsheng; Lu, Dan; Cui, Mingde

2018-01-01

In order to study the stability of power system, a power system transient stability based on data mining theory is designed. By introducing association rules analysis in data mining theory, an association classification method for transient stability assessment is presented. A mathematical model of transient stability assessment based on data mining technology is established. Meanwhile, combining rule reasoning with classification prediction, the method of association classification is proposed to perform transient stability assessment. The transient stability index is used to identify the samples that cannot be correctly classified in association classification. Then, according to the critical stability of each sample, the time domain simulation method is used to determine the state, so as to ensure the accuracy of the final results. The results show that this stability assessment system can improve the speed of operation under the premise that the analysis result is completely correct, and the improved algorithm can find out the inherent relation between the change of power system operation mode and the change of transient stability degree.

Weighted Association Rule Mining for Item Groups with Different Properties and Risk Assessment for Networked Systems

NASA Astrophysics Data System (ADS)

Kim, Jungja; Ceong, Heetaek; Won, Yonggwan

In market-basket analysis, weighted association rule (WAR) discovery can mine the rules that include more beneficial information by reflecting item importance for special products. In the point-of-sale database, each transaction is composed of items with similar properties, and item weights are pre-defined and fixed by a factor such as the profit. However, when items are divided into more than one group and the item importance must be measured independently for each group, traditional weighted association rule discovery cannot be used. To solve this problem, we propose a new weighted association rule mining methodology. The items should be first divided into subgroups according to their properties, and the item importance, i.e. item weight, is defined or calculated only with the items included in the subgroup. Then, transaction weight is measured by appropriately summing the item weights from each subgroup, and the weighted support is computed as the fraction of the transaction weights that contains the candidate items relative to the weight of all transactions. As an example, our proposed methodology is applied to assess the vulnerability to threats of computer systems that provide networked services. Our algorithm provides both quantitative risk-level values and qualitative risk rules for the security assessment of networked computer systems using WAR discovery. Also, it can be widely used for new applications with many data sets in which the data items are distinctly separated.

Association rule mining in the US Vaccine Adverse Event Reporting System (VAERS).

PubMed

Wei, Lai; Scott, John

2015-09-01

Spontaneous adverse event reporting systems are critical tools for monitoring the safety of licensed medical products. Commonly used signal detection algorithms identify disproportionate product-adverse event pairs and may not be sensitive to more complex potential signals. We sought to develop a computationally tractable multivariate data-mining approach to identify product-multiple adverse event associations. We describe an application of stepwise association rule mining (Step-ARM) to detect potential vaccine-symptom group associations in the US Vaccine Adverse Event Reporting System. Step-ARM identifies strong associations between one vaccine and one or more adverse events. To reduce the number of redundant association rules found by Step-ARM, we also propose a clustering method for the post-processing of association rules. In sample applications to a trivalent intradermal inactivated influenza virus vaccine and to measles, mumps, rubella, and varicella (MMRV) vaccine and in simulation studies, we find that Step-ARM can detect a variety of medically coherent potential vaccine-symptom group signals efficiently. In the MMRV example, Step-ARM appears to outperform univariate methods in detecting a known safety signal. Our approach is sensitive to potentially complex signals, which may be particularly important when monitoring novel medical countermeasure products such as pandemic influenza vaccines. The post-processing clustering algorithm improves the applicability of the approach as a screening method to identify patterns that may merit further investigation. Copyright © 2015 John Wiley & Sons, Ltd.

Compass: a hybrid method for clinical and biobank data mining.

PubMed

Krysiak-Baltyn, K; Nordahl Petersen, T; Audouze, K; Jørgensen, Niels; Angquist, L; Brunak, S

2014-02-01

We describe a new method for identification of confident associations within large clinical data sets. The method is a hybrid of two existing methods; Self-Organizing Maps and Association Mining. We utilize Self-Organizing Maps as the initial step to reduce the search space, and then apply Association Mining in order to find association rules. We demonstrate that this procedure has a number of advantages compared to traditional Association Mining; it allows for handling numerical variables without a priori binning and is able to generate variable groups which act as "hotspots" for statistically significant associations. We showcase the method on infertility-related data from Danish military conscripts. The clinical data we analyzed contained both categorical type questionnaire data and continuous variables generated from biological measurements, including missing values. From this data set, we successfully generated a number of interesting association rules, which relate an observation with a specific consequence and the p-value for that finding. Additionally, we demonstrate that the method can be used on non-clinical data containing chemical-disease associations in order to find associations between different phenotypes, such as prostate cancer and breast cancer. Copyright © 2013 Elsevier Inc. All rights reserved.

Analysis of North Atlantic tropical cyclone intensify change using data mining

NASA Astrophysics Data System (ADS)

Tang, Jiang

Tropical cyclones (TC), especially when their intensity reaches hurricane scale, can become a costly natural hazard. Accurate prediction of tropical cyclone intensity is very difficult because of inadequate observations on TC structures, poor understanding of physical processes, coarse model resolution and inaccurate initial conditions, etc. This study aims to tackle two factors that account for the underperformance of current TC intensity forecasts: (1) inadequate observations of TC structures, and (2) deficient understanding of the underlying physical processes governing TC intensification. To tackle the problem of inadequate observations of TC structures, efforts have been made to extract vertical and horizontal structural parameters of latent heat release from Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) data products. A case study of Hurricane Isabel (2003) was conducted first to explore the feasibility of using the 3D TC structure information in predicting TC intensification. Afterwards, several structural parameters were extracted from 53 TRMM PR 2A25 observations on 25 North Atlantic TCs during the period of 1998 to 2003. A new generation of multi-correlation data mining algorithm (Apriori and its variations) was applied to find roles of the latent heat release structure in TC intensification. The results showed that the buildup of TC energy is indicated by the height of the convective tower, and the relative low latent heat release at the core area and around the outer band. Adverse conditions which prevent TC intensification include the following: (1) TC entering a higher latitude area where the underlying sea is relative cold, (2) TC moving too fast to absorb the thermal energy from the underlying sea, or (3) strong energy loss at the outer band. When adverse conditions and amicable conditions reached equilibrium status, tropical cyclone intensity would remain stable. The dataset from Statistical Hurricane Intensity Prediction Scheme (SHIPS) covering the period of 1982-2003 and the Apriori-based association rule mining algorithm were used to study the associations of underlying geophysical characteristics with the intensity change of tropical cyclones. The data have been stratified into 6 TC categories from tropical depression to category 4 hurricanes based on their strength. The result showed that the persistence of intensity change in the past and the strength of vertical shear in the environment are the most prevalent factors for all of the 6 TC categories. Hyper-edge searching had found 3 sets of parameters which showed strong intramural binds. Most of the parameters used in SHIPS model have a consistent "I-W" relation over different TC categories, indicating a consistent function of those parameters in TC development. However, the "I-W" relations of the relative momentum flux and the meridional motion change from tropical storm stage to hurricane stage, indicating a change in the role of those two parameters in TC development. Because rapid intensification (RI) is a major source of errors when predicting hurricane intensity, the association rule mining algorithm was performed on RI versus non-RI tropical cyclone cases using the same SHIPS dataset. The results had been compared with those from the traditional statistical analysis conducted by Kaplan and DeMaria (2003). The rapid intensification rule with 5 RI conditions proposed by the traditional statistical analysis was found by the association rule mining in this study as well. However, further analysis showed that the 5 RI conditions can be replaced by another association rule using fewer conditions but with a higher RI probability (RIP). This means that the rule with all 5 constraints found by Kaplan and DeMaria is not optimal, and the association rule mining technique can find a rule with fewer constraints yet fits more RI cases. The further analysis with the highest RIPs over different numbers of conditions has demonstrated that the interactions among multiple factors are responsible for the RI process of TCs. However, the influence of factors saturates at certain numbers. This study has shown successful data mining examples in studying tropical cyclone intensification using association rules. The higher RI probability with fewer conditions found by association rule technique is significant. This work demonstrated that data mining techniques can be used as an efficient exploration method to generate hypotheses, and that statistical analysis should be performed to confirm the hypotheses, as is generally expected for data mining applications.

Temporal data mining for the quality assessment of hemodialysis services.

PubMed

Bellazzi, Riccardo; Larizza, Cristiana; Magni, Paolo; Bellazzi, Roberto

2005-05-01

This paper describes the temporal data mining aspects of a research project that deals with the definition of methods and tools for the assessment of the clinical performance of hemodialysis (HD) services, on the basis of the time series automatically collected during hemodialysis sessions. Intelligent data analysis and temporal data mining techniques are applied to gain insight and to discover knowledge on the causes of unsatisfactory clinical results. In particular, two new methods for association rule discovery and temporal rule discovery are applied to the time series. Such methods exploit several pre-processing techniques, comprising data reduction, multi-scale filtering and temporal abstractions. We have analyzed the data of more than 5800 dialysis sessions coming from 43 different patients monitored for 19 months. The qualitative rules associating the outcome parameters and the measured variables were examined by the domain experts, which were able to distinguish between rules confirming available background knowledge and unexpected but plausible rules. The new methods proposed in the paper are suitable tools for knowledge discovery in clinical time series. Their use in the context of an auditing system for dialysis management helped clinicians to improve their understanding of the patients' behavior.

Validity of association rules extracted by healthcare-data-mining.

PubMed

Takeuchi, Hiroshi; Kodama, Naoki

2014-01-01

A personal healthcare system used with cloud computing has been developed. It enables a daily time-series of personal health and lifestyle data to be stored in the cloud through mobile devices. The cloud automatically extracts personally useful information, such as rules and patterns concerning the user's lifestyle and health condition embedded in their personal big data, by using healthcare-data-mining. This study has verified that the extracted rules on the basis of a daily time-series data stored during a half- year by volunteer users of this system are valid.

A comprehensive review on privacy preserving data mining.

PubMed

Aldeen, Yousra Abdul Alsahib S; Salleh, Mazleena; Razzaque, Mohammad Abdur

2015-01-01

Preservation of privacy in data mining has emerged as an absolute prerequisite for exchanging confidential information in terms of data analysis, validation, and publishing. Ever-escalating internet phishing posed severe threat on widespread propagation of sensitive information over the web. Conversely, the dubious feelings and contentions mediated unwillingness of various information providers towards the reliability protection of data from disclosure often results utter rejection in data sharing or incorrect information sharing. This article provides a panoramic overview on new perspective and systematic interpretation of a list published literatures via their meticulous organization in subcategories. The fundamental notions of the existing privacy preserving data mining methods, their merits, and shortcomings are presented. The current privacy preserving data mining techniques are classified based on distortion, association rule, hide association rule, taxonomy, clustering, associative classification, outsourced data mining, distributed, and k-anonymity, where their notable advantages and disadvantages are emphasized. This careful scrutiny reveals the past development, present research challenges, future trends, the gaps and weaknesses. Further significant enhancements for more robust privacy protection and preservation are affirmed to be mandatory.

Association rule mining on grid monitoring data to detect error sources

NASA Astrophysics Data System (ADS)

Maier, Gerhild; Schiffers, Michael; Kranzlmueller, Dieter; Gaidioz, Benjamin

2010-04-01

Error handling is a crucial task in an infrastructure as complex as a grid. There are several monitoring tools put in place, which report failing grid jobs including exit codes. However, the exit codes do not always denote the actual fault, which caused the job failure. Human time and knowledge is required to manually trace back errors to the real fault underlying an error. We perform association rule mining on grid job monitoring data to automatically retrieve knowledge about the grid components' behavior by taking dependencies between grid job characteristics into account. Therewith, problematic grid components are located automatically and this information - expressed by association rules - is visualized in a web interface. This work achieves a decrease in time for fault recovery and yields an improvement of a grid's reliability.

Validation of an association rule mining-based method to infer associations between medications and problems.

PubMed

Wright, A; McCoy, A; Henkin, S; Flaherty, M; Sittig, D

2013-01-01

In a prior study, we developed methods for automatically identifying associations between medications and problems using association rule mining on a large clinical data warehouse and validated these methods at a single site which used a self-developed electronic health record. To demonstrate the generalizability of these methods by validating them at an external site. We received data on medications and problems for 263,597 patients from the University of Texas Health Science Center at Houston Faculty Practice, an ambulatory practice that uses the Allscripts Enterprise commercial electronic health record product. We then conducted association rule mining to identify associated pairs of medications and problems and characterized these associations with five measures of interestingness: support, confidence, chi-square, interest and conviction and compared the top-ranked pairs to a gold standard. 25,088 medication-problem pairs were identified that exceeded our confidence and support thresholds. An analysis of the top 500 pairs according to each measure of interestingness showed a high degree of accuracy for highly-ranked pairs. The same technique was successfully employed at the University of Texas and accuracy was comparable to our previous results. Top associations included many medications that are highly specific for a particular problem as well as a large number of common, accurate medication-problem pairs that reflect practice patterns.

DTFP-Growth: Dynamic Threshold-Based FP-Growth Rule Mining Algorithm Through Integrating Gene Expression, Methylation, and Protein-Protein Interaction Profiles.

PubMed

Mallik, Saurav; Bhadra, Tapas; Mukherji, Ayan; Mallik, Saurav; Bhadra, Tapas; Mukherji, Ayan; Mallik, Saurav; Bhadra, Tapas; Mukherji, Ayan

2018-04-01

Association rule mining is an important technique for identifying interesting relationships between gene pairs in a biological data set. Earlier methods basically work for a single biological data set, and, in maximum cases, a single minimum support cutoff can be applied globally, i.e., across all genesets/itemsets. To overcome this limitation, in this paper, we propose dynamic threshold-based FP-growth rule mining algorithm that integrates gene expression, methylation and protein-protein interaction profiles based on weighted shortest distance to find the novel associations among different pairs of genes in multi-view data sets. For this purpose, we introduce three new thresholds, namely, Distance-based Variable/Dynamic Supports (DVS), Distance-based Variable Confidences (DVC), and Distance-based Variable Lifts (DVL) for each rule by integrating co-expression, co-methylation, and protein-protein interactions existed in the multi-omics data set. We develop the proposed algorithm utilizing these three novel multiple threshold measures. In the proposed algorithm, the values of , , and are computed for each rule separately, and subsequently it is verified whether the support, confidence, and lift of each evolved rule are greater than or equal to the corresponding individual , , and values, respectively, or not. If all these three conditions for a rule are found to be true, the rule is treated as a resultant rule. One of the major advantages of the proposed method compared with other related state-of-the-art methods is that it considers both the quantitative and interactive significance among all pairwise genes belonging to each rule. Moreover, the proposed method generates fewer rules, takes less running time, and provides greater biological significance for the resultant top-ranking rules compared to previous methods.

Safety rules and regulations on mine sites - the problem and a solution.

PubMed

Laurence, David

2005-01-01

Many accidents and incidents on mine sites have a causal factor in the rules and regulations that supposedly are in place to prevent the incident from occurring. The causes involve a lack of awareness or understanding, ignorance, or deliberate violations. The issue of mine rules, procedures, and regulations is a central focus of this paper, highlighted by this recent comment - "very few people have accidents for which there is no procedure in place..." An attitudinal survey was conducted at 33 mines throughout NSW, Queensland and international mine sites involving almost 500 mineworkers. The survey was in the form of a self-completing questionnaire, consisting of approximately 65 questions. It aimed to seek the opinions of the mining workforce on safety rules and regulations generally, as well as how they apply to their specific jobs on a mine site. The research also aimed to investigate: (a) the level of awareness and understanding of mine rules and procedures such as manager's rules and safe work procedures (SWPs); (b) the level of awareness and understanding of mine safety regulations and legislation; (c) the extent of communication of and commitment to rules and regulations; (d) the extent of compliance with rules and regulations; and (e) attitudes regarding errors, risk-taking, and accidents and their interaction with rules and regulations. The sample consisted of a random selection of underground and open pit mines, extracting coal, metals, or industrial minerals. The insights provided by the mineworkers enabled a set of principles to be developed to guide mine management and regulators in the development of more effective rules and regulations. CONCLUSIONS AND IMPACT ON THE MINING INDUSTRY: (a) Management and regulators should not continue to produce more and more rules and regulations to cover every aspect of mining. (b) Detailed prescriptive regulations, detailed safe work procedures, and voluminous safety management plans will not "connect" with a miner. (c) Achieving more effective rules and regulations is not the only answer to a safer workplace.

A Novel Biclustering Approach to Association Rule Mining for Predicting HIV-1–Human Protein Interactions

PubMed Central

Mukhopadhyay, Anirban; Maulik, Ujjwal; Bandyopadhyay, Sanghamitra

2012-01-01

Identification of potential viral-host protein interactions is a vital and useful approach towards development of new drugs targeting those interactions. In recent days, computational tools are being utilized for predicting viral-host interactions. Recently a database containing records of experimentally validated interactions between a set of HIV-1 proteins and a set of human proteins has been published. The problem of predicting new interactions based on this database is usually posed as a classification problem. However, posing the problem as a classification one suffers from the lack of biologically validated negative interactions. Therefore it will be beneficial to use the existing database for predicting new viral-host interactions without the need of negative samples. Motivated by this, in this article, the HIV-1–human protein interaction database has been analyzed using association rule mining. The main objective is to identify a set of association rules both among the HIV-1 proteins and among the human proteins, and use these rules for predicting new interactions. In this regard, a novel association rule mining technique based on biclustering has been proposed for discovering frequent closed itemsets followed by the association rules from the adjacency matrix of the HIV-1–human interaction network. Novel HIV-1–human interactions have been predicted based on the discovered association rules and tested for biological significance. For validation of the predicted new interactions, gene ontology-based and pathway-based studies have been performed. These studies show that the human proteins which are predicted to interact with a particular viral protein share many common biological activities. Moreover, literature survey has been used for validation purpose to identify some predicted interactions that are already validated experimentally but not present in the database. Comparison with other prediction methods is also discussed. PMID:22539940

Association Rule Based Feature Extraction for Character Recognition

NASA Astrophysics Data System (ADS)

Dua, Sumeet; Singh, Harpreet

Association rules that represent isomorphisms among data have gained importance in exploratory data analysis because they can find inherent, implicit, and interesting relationships among data. They are also commonly used in data mining to extract the conditions among attribute values that occur together frequently in a dataset [1]. These rules have wide range of applications, namely in the financial and retail sectors of marketing, sales, and medicine.

Mining knowledge from corpora: an application to retrieval and indexing.

PubMed

Soualmia, Lina F; Dahamna, Badisse; Darmoni, Stéfan

2008-01-01

The present work aims at discovering new associations between medical concepts to be exploited as input in retrieval and indexing. Association rules method is applied to documents. The process is carried out on three major document categories referring to e-health information consumers: health professionals, students and lay people. Association rules evaluation is founded on statistical measures combined with domain knowledge. Association rules represent existing relations between medical concepts (60.62%) and new knowledge (54.21%). Based on observations, 463 expert rules are defined by medical librarians for retrieval and indexing. Association rules bear out existing relations, produce new knowledge and support users and indexers in document retrieval and indexing.

A Bayesian Scoring Technique for Mining Predictive and Non-Spurious Rules

PubMed Central

Batal, Iyad; Cooper, Gregory; Hauskrecht, Milos

2015-01-01

Rule mining is an important class of data mining methods for discovering interesting patterns in data. The success of a rule mining method heavily depends on the evaluation function that is used to assess the quality of the rules. In this work, we propose a new rule evaluation score - the Predictive and Non-Spurious Rules (PNSR) score. This score relies on Bayesian inference to evaluate the quality of the rules and considers the structure of the rules to filter out spurious rules. We present an efficient algorithm for finding rules with high PNSR scores. The experiments demonstrate that our method is able to cover and explain the data with a much smaller rule set than existing methods. PMID:25938136

A Bayesian Scoring Technique for Mining Predictive and Non-Spurious Rules.

PubMed

Batal, Iyad; Cooper, Gregory; Hauskrecht, Milos

Rule mining is an important class of data mining methods for discovering interesting patterns in data. The success of a rule mining method heavily depends on the evaluation function that is used to assess the quality of the rules. In this work, we propose a new rule evaluation score - the Predictive and Non-Spurious Rules (PNSR) score. This score relies on Bayesian inference to evaluate the quality of the rules and considers the structure of the rules to filter out spurious rules. We present an efficient algorithm for finding rules with high PNSR scores. The experiments demonstrate that our method is able to cover and explain the data with a much smaller rule set than existing methods.

Efficient discovery of risk patterns in medical data.

PubMed

Li, Jiuyong; Fu, Ada Wai-chee; Fahey, Paul

2009-01-01

This paper studies a problem of efficiently discovering risk patterns in medical data. Risk patterns are defined by a statistical metric, relative risk, which has been widely used in epidemiological research. To avoid fruitless search in the complete exploration of risk patterns, we define optimal risk pattern set to exclude superfluous patterns, i.e. complicated patterns with lower relative risk than their corresponding simpler form patterns. We prove that mining optimal risk pattern sets conforms an anti-monotone property that supports an efficient mining algorithm. We propose an efficient algorithm for mining optimal risk pattern sets based on this property. We also propose a hierarchical structure to present discovered patterns for the easy perusal by domain experts. The proposed approach is compared with two well-known rule discovery methods, decision tree and association rule mining approaches on benchmark data sets and applied to a real world application. The proposed method discovers more and better quality risk patterns than a decision tree approach. The decision tree method is not designed for such applications and is inadequate for pattern exploring. The proposed method does not discover a large number of uninteresting superfluous patterns as an association mining approach does. The proposed method is more efficient than an association rule mining method. A real world case study shows that the method reveals some interesting risk patterns to medical practitioners. The proposed method is an efficient approach to explore risk patterns. It quickly identifies cohorts of patients that are vulnerable to a risk outcome from a large data set. The proposed method is useful for exploratory study on large medical data to generate and refine hypotheses. The method is also useful for designing medical surveillance systems.

Association-rule-based tuberculosis disease diagnosis

NASA Astrophysics Data System (ADS)

Asha, T.; Natarajan, S.; Murthy, K. N. B.

2010-02-01

Tuberculosis (TB) is a disease caused by bacteria called Mycobacterium tuberculosis. It usually spreads through the air and attacks low immune bodies such as patients with Human Immunodeficiency Virus (HIV). This work focuses on finding close association rules, a promising technique in Data Mining, within TB data. The proposed method first normalizes of raw data from medical records which includes categorical, nominal and continuous attributes and then determines Association Rules from the normalized data with different support and confidence. Association rules are applied on a real data set containing medical records of patients with TB obtained from a state hospital. The rules determined describes close association between one symptom to another; as an example, likelihood that an occurrence of sputum is closely associated with blood cough and HIV.

Clustering and Dimensionality Reduction to Discover Interesting Patterns in Binary Data

NASA Astrophysics Data System (ADS)

Palumbo, Francesco; D'Enza, Alfonso Iodice

The attention towards binary data coding increased consistently in the last decade due to several reasons. The analysis of binary data characterizes several fields of application, such as market basket analysis, DNA microarray data, image mining, text mining and web-clickstream mining. The paper illustrates two different approaches exploiting a profitable combination of clustering and dimensionality reduction for the identification of non-trivial association structures in binary data. An application in the Association Rules framework supports the theory with the empirical evidence.

76 FR 63238 - Proximity Detection Systems for Continuous Mining Machines in Underground Coal Mines

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-12

... Detection Systems for Continuous Mining Machines in Underground Coal Mines AGENCY: Mine Safety and Health... Agency's proposed rule addressing Proximity Detection Systems for Continuous Mining Machines in... proposed rule for Proximity Detection Systems on Continuous Mining Machines in Underground Coal Mines. Due...

Mining Research on Vibration Signal Association Rules of Quayside Container Crane Hoisting Motor Based on Apriori Algorithm

NASA Astrophysics Data System (ADS)

Yang, Chencheng; Tang, Gang; Hu, Xiong

2017-07-01

Shore-hoisting motor in the daily work will produce a large number of vibration signal data,in order to analyze the correlation among the data and discover the fault and potential safety hazard of the motor, the data are discretized first, and then Apriori algorithm are used to mine the strong association rules among the data. The results show that the relationship between day 1 and day 16 is the most closely related, which can guide the staff to analyze the work of these two days of motor to find and solve the problem of fault and safety.

Exploration of association rule mining for coding consistency and completeness assessment in inpatient administrative health data.

PubMed

Peng, Mingkai; Sundararajan, Vijaya; Williamson, Tyler; Minty, Evan P; Smith, Tony C; Doktorchik, Chelsea T A; Quan, Hude

2018-03-01

Data quality assessment is a challenging facet for research using coded administrative health data. Current assessment approaches are time and resource intensive. We explored whether association rule mining (ARM) can be used to develop rules for assessing data quality. We extracted 2013 and 2014 records from the hospital discharge abstract database (DAD) for patients between the ages of 55 and 65 from five acute care hospitals in Alberta, Canada. The ARM was conducted using the 2013 DAD to extract rules with support ≥0.0019 and confidence ≥0.5 using the bootstrap technique, and tested in the 2014 DAD. The rules were compared against the method of coding frequency and assessed for their ability to detect error introduced by two kinds of data manipulation: random permutation and random deletion. The association rules generally had clear clinical meanings. Comparing 2014 data to 2013 data (both original), there were 3 rules with a confidence difference >0.1, while coding frequency difference of codes in the right hand of rules was less than 0.004. After random permutation of 50% of codes in the 2014 data, average rule confidence dropped from 0.72 to 0.27 while coding frequency remained unchanged. Rule confidence decreased with the increase of coding deletion, as expected. Rule confidence was more sensitive to code deletion compared to coding frequency, with slope of change ranging from 1.7 to 184.9 with a median of 9.1. The ARM is a promising technique to assess data quality. It offers a systematic way to derive coding association rules hidden in data, and potentially provides a sensitive and efficient method of assessing data quality compared to standard methods. Copyright © 2018 Elsevier Inc. All rights reserved.

Analysis on composition rules of Chinese patent drugs treating pain-related diseases based on data mining method.

PubMed

Tang, Shi-Huan; Shen, Dan; Yang, Hong-Jun

2017-08-24

To analyze the composition rules of oral prescriptions in the treatment of headache, stomachache and dysmenorrhea recorded in National Standard for Chinese Patent Drugs (NSCPD) enacted by Ministry of Public Health of China and then make comparison between them to better understand pain treatment in different regions of human body. Constructed NSCPD database had been constructed in 2014. Prescriptions treating the three pain-related diseases were searched and screened from the database. Then data mining method such as association rules analysis and complex system entropy method integrated in the data mining software Traditional Chinese Medicine Inheritance Support System (TCMISS) were applied to process the data. Top 25 drugs with high frequency in the treatment of each disease were selected, and 51, 33 and 22 core combinations treating headache, stomachache and dysmenorrhea respectively were mined out as well. The composition rules of the oral prescriptions for treating headache, stomachache and dysmenorrhea recorded in NSCPD has been summarized. Although there were similarities between them, formula varied according to different locations of pain. It can serve as an evidence and reference for clinical treatment and new drug development.

Microbial genotype-phenotype mapping by class association rule mining.

PubMed

Tamura, Makio; D'haeseleer, Patrik

2008-07-01

Microbial phenotypes are typically due to the concerted action of multiple gene functions, yet the presence of each gene may have only a weak correlation with the observed phenotype. Hence, it may be more appropriate to examine co-occurrence between sets of genes and a phenotype (multiple-to-one) instead of pairwise relations between a single gene and the phenotype. Here, we propose an efficient class association rule mining algorithm, netCAR, in order to extract sets of COGs (clusters of orthologous groups of proteins) associated with a phenotype from COG phylogenetic profiles and a phenotype profile. netCAR takes into account the phylogenetic co-occurrence graph between COGs to restrict hypothesis space, and uses mutual information to evaluate the biconditional relation. We examined the mining capability of pairwise and multiple-to-one association by using netCAR to extract COGs relevant to six microbial phenotypes (aerobic, anaerobic, facultative, endospore, motility and Gram negative) from 11,969 unique COG profiles across 155 prokaryotic organisms. With the same level of false discovery rate, multiple-to-one association can extract about 10 times more relevant COGs than one-to-one association. We also reveal various topologies of association networks among COGs (modules) from extracted multiple-to-one correlation rules relevant with the six phenotypes; including a well-connected network for motility, a star-shaped network for aerobic and intermediate topologies for the other phenotypes. netCAR outperforms a standard CAR mining algorithm, CARapriori, while requiring several orders of magnitude less computational time for extracting 3-COG sets. Source code of the Java implementation is available as Supplementary Material at the Bioinformatics online website, or upon request to the author. Supplementary data are available at Bioinformatics online.

Exploration of the association rules mining technique for the signal detection of adverse drug events in spontaneous reporting systems.

PubMed

Wang, Chao; Guo, Xiao-Jing; Xu, Jin-Fang; Wu, Cheng; Sun, Ya-Lin; Ye, Xiao-Fei; Qian, Wei; Ma, Xiu-Qiang; Du, Wen-Min; He, Jia

2012-01-01

The detection of signals of adverse drug events (ADEs) has increased because of the use of data mining algorithms in spontaneous reporting systems (SRSs). However, different data mining algorithms have different traits and conditions for application. The objective of our study was to explore the application of association rule (AR) mining in ADE signal detection and to compare its performance with that of other algorithms. Monte Carlo simulation was applied to generate drug-ADE reports randomly according to the characteristics of SRS datasets. Thousand simulated datasets were mined by AR and other algorithms. On average, 108,337 reports were generated by the Monte Carlo simulation. Based on the predefined criterion that 10% of the drug-ADE combinations were true signals, with RR equaling to 10, 4.9, 1.5, and 1.2, AR detected, on average, 284 suspected associations with a minimum support of 3 and a minimum lift of 1.2. The area under the receiver operating characteristic (ROC) curve of the AR was 0.788, which was equivalent to that shown for other algorithms. Additionally, AR was applied to reports submitted to the Shanghai SRS in 2009. Five hundred seventy combinations were detected using AR from 24,297 SRS reports, and they were compared with recognized ADEs identified by clinical experts and various other sources. AR appears to be an effective method for ADE signal detection, both in simulated and real SRS datasets. The limitations of this method exposed in our study, i.e., a non-uniform thresholds setting and redundant rules, require further research.

Effective application of improved profit-mining algorithm for the interday trading model.

PubMed

Hsieh, Yu-Lung; Yang, Don-Lin; Wu, Jungpin

2014-01-01

Many real world applications of association rule mining from large databases help users make better decisions. However, they do not work well in financial markets at this time. In addition to a high profit, an investor also looks for a low risk trading with a better rate of winning. The traditional approach of using minimum confidence and support thresholds needs to be changed. Based on an interday model of trading, we proposed effective profit-mining algorithms which provide investors with profit rules including information about profit, risk, and winning rate. Since profit-mining in the financial market is still in its infant stage, it is important to detail the inner working of mining algorithms and illustrate the best way to apply them. In this paper we go into details of our improved profit-mining algorithm and showcase effective applications with experiments using real world trading data. The results show that our approach is practical and effective with good performance for various datasets.

Effective Application of Improved Profit-Mining Algorithm for the Interday Trading Model

PubMed Central

Wu, Jungpin

2014-01-01

Many real world applications of association rule mining from large databases help users make better decisions. However, they do not work well in financial markets at this time. In addition to a high profit, an investor also looks for a low risk trading with a better rate of winning. The traditional approach of using minimum confidence and support thresholds needs to be changed. Based on an interday model of trading, we proposed effective profit-mining algorithms which provide investors with profit rules including information about profit, risk, and winning rate. Since profit-mining in the financial market is still in its infant stage, it is important to detail the inner working of mining algorithms and illustrate the best way to apply them. In this paper we go into details of our improved profit-mining algorithm and showcase effective applications with experiments using real world trading data. The results show that our approach is practical and effective with good performance for various datasets. PMID:24688442

Rule Mining Techniques to Predict Prokaryotic Metabolic Pathways.

PubMed

Saidi, Rabie; Boudellioua, Imane; Martin, Maria J; Solovyev, Victor

2017-01-01

It is becoming more evident that computational methods are needed for the identification and the mapping of pathways in new genomes. We introduce an automatic annotation system (ARBA4Path Association Rule-Based Annotator for Pathways) that utilizes rule mining techniques to predict metabolic pathways across wide range of prokaryotes. It was demonstrated that specific combinations of protein domains (recorded in our rules) strongly determine pathways in which proteins are involved and thus provide information that let us very accurately assign pathway membership (with precision of 0.999 and recall of 0.966) to proteins of a given prokaryotic taxon. Our system can be used to enhance the quality of automatically generated annotations as well as annotating proteins with unknown function. The prediction models are represented in the form of human-readable rules, and they can be used effectively to add absent pathway information to many proteins in UniProtKB/TrEMBL database.

26 CFR 1.611-2 - Rules applicable to mines, oil and gas wells, and other natural deposits.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 26 Internal Revenue 7 2013-04-01 2013-04-01 false Rules applicable to mines, oil and gas wells....611-2 Rules applicable to mines, oil and gas wells, and other natural deposits. (a) Computation of cost depletion of mines, oil and gas wells, and other natural deposits. (1) The basis upon which cost...

26 CFR 1.611-2 - Rules applicable to mines, oil and gas wells, and other natural deposits.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 26 Internal Revenue 7 2012-04-01 2012-04-01 false Rules applicable to mines, oil and gas wells....611-2 Rules applicable to mines, oil and gas wells, and other natural deposits. (a) Computation of cost depletion of mines, oil and gas wells, and other natural deposits. (1) The basis upon which cost...

Co-evolutionary data mining for fuzzy rules: automatic fitness function creation phase space, and experiments

NASA Astrophysics Data System (ADS)

Smith, James F., III; Blank, Joseph A.

2003-03-01

An approach is being explored that involves embedding a fuzzy logic based resource manager in an electronic game environment. Game agents can function under their own autonomous logic or human control. This approach automates the data mining problem. The game automatically creates a cleansed database reflecting the domain expert's knowledge, it calls a data mining function, a genetic algorithm, for data mining of the data base as required and allows easy evaluation of the information extracted. The co-evolutionary fitness functions, chromosomes and stopping criteria for ending the game are discussed. Genetic algorithm and genetic program based data mining procedures are discussed that automatically discover new fuzzy rules and strategies. The strategy tree concept and its relationship to co-evolutionary data mining are examined as well as the associated phase space representation of fuzzy concepts. The overlap of fuzzy concepts in phase space reduces the effective strategies available to adversaries. Co-evolutionary data mining alters the geometric properties of the overlap region known as the admissible region of phase space significantly enhancing the performance of the resource manager. Procedures for validation of the information data mined are discussed and significant experimental results provided.

Spatio-Temporal Pattern Mining on Trajectory Data Using Arm

NASA Astrophysics Data System (ADS)

Khoshahval, S.; Farnaghi, M.; Taleai, M.

2017-09-01

Preliminary mobile was considered to be a device to make human connections easier. But today the consumption of this device has been evolved to a platform for gaming, web surfing and GPS-enabled application capabilities. Embedding GPS in handheld devices, altered them to significant trajectory data gathering facilities. Raw GPS trajectory data is a series of points which contains hidden information. For revealing hidden information in traces, trajectory data analysis is needed. One of the most beneficial concealed information in trajectory data is user activity patterns. In each pattern, there are multiple stops and moves which identifies users visited places and tasks. This paper proposes an approach to discover user daily activity patterns from GPS trajectories using association rules. Finding user patterns needs extraction of user's visited places from stops and moves of GPS trajectories. In order to locate stops and moves, we have implemented a place recognition algorithm. After extraction of visited points an advanced association rule mining algorithm, called Apriori was used to extract user activity patterns. This study outlined that there are useful patterns in each trajectory that can be emerged from raw GPS data using association rule mining techniques in order to find out about multiple users' behaviour in a system and can be utilized in various location-based applications.

Frequent Itemset Hiding Algorithm Using Frequent Pattern Tree Approach

ERIC Educational Resources Information Center

Alnatsheh, Rami

2012-01-01

A problem that has been the focus of much recent research in privacy preserving data-mining is the frequent itemset hiding (FIH) problem. Identifying itemsets that appear together frequently in customer transactions is a common task in association rule mining. Organizations that share data with business partners may consider some of the frequent…

Analysis of correlation between pediatric asthma exacerbation and exposure to pollutant mixtures with association rule mining.

PubMed

Toti, Giulia; Vilalta, Ricardo; Lindner, Peggy; Lefer, Barry; Macias, Charles; Price, Daniel

2016-11-01

Traditional studies on effects of outdoor pollution on asthma have been criticized for questionable statistical validity and inefficacy in exploring the effects of multiple air pollutants, alone and in combination. Association rule mining (ARM), a method easily interpretable and suitable for the analysis of the effects of multiple exposures, could be of use, but the traditional interest metrics of support and confidence need to be substituted with metrics that focus on risk variations caused by different exposures. We present an ARM-based methodology that produces rules associated with relevant odds ratios and limits the number of final rules even at very low support levels (0.5%), thanks to post-pruning criteria that limit rule redundancy and control for statistical significance. The methodology has been applied to a case-crossover study to explore the effects of multiple air pollutants on risk of asthma in pediatric subjects. We identified 27 rules with interesting odds ratio among more than 10,000 having the required support. The only rule including only one chemical is exposure to ozone on the previous day of the reported asthma attack (OR=1.14). 26 combinatory rules highlight the limitations of air quality policies based on single pollutant thresholds and suggest that exposure to mixtures of chemicals is more harmful, with odds ratio as high as 1.54 (associated with the combination day0 SO 2 , day0 NO, day0 NO 2 , day1 PM). The proposed method can be used to analyze risk variations caused by single and multiple exposures. The method is reliable and requires fewer assumptions on the data than parametric approaches. Rules including more than one pollutant highlight interactions that deserve further investigation, while helping to limit the search field. Copyright © 2016 Elsevier B.V. All rights reserved.

An application of data mining in district heating substations for improving energy performance

NASA Astrophysics Data System (ADS)

Xue, Puning; Zhou, Zhigang; Chen, Xin; Liu, Jing

2017-11-01

Automatic meter reading system is capable of collecting and storing a huge number of district heating (DH) data. However, the data obtained are rarely fully utilized. Data mining is a promising technology to discover potential interesting knowledge from vast data. This paper applies data mining methods to analyse the massive data for improving energy performance of DH substation. The technical approach contains three steps: data selection, cluster analysis and association rule mining (ARM). Two-heating-season data of a substation are used for case study. Cluster analysis identifies six distinct heating patterns based on the primary heat of the substation. ARM reveals that secondary pressure difference and secondary flow rate have a strong correlation. Using the discovered rules, a fault occurring in remote flow meter installed at secondary network is detected accurately. The application demonstrates that data mining techniques can effectively extrapolate potential useful knowledge to better understand substation operation strategies and improve substation energy performance.

Recommendation System Based On Association Rules For Distributed E-Learning Management Systems

NASA Astrophysics Data System (ADS)

Mihai, Gabroveanu

2015-09-01

Traditional Learning Management Systems are installed on a single server where learning materials and user data are kept. To increase its performance, the Learning Management System can be installed on multiple servers; learning materials and user data could be distributed across these servers obtaining a Distributed Learning Management System. In this paper is proposed the prototype of a recommendation system based on association rules for Distributed Learning Management System. Information from LMS databases is analyzed using distributed data mining algorithms in order to extract the association rules. Then the extracted rules are used as inference rules to provide personalized recommendations. The quality of provided recommendations is improved because the rules used to make the inferences are more accurate, since these rules aggregate knowledge from all e-Learning systems included in Distributed Learning Management System.

Quantifying Associations between Environmental and Social Stressors

EPA Science Inventory

Introduction: Association rule mining (ARM) has been widely used to identify associations between various entities in many fields. Although some studies have utilized it to analyze the relationship between chemicals and human effects, fewer have used this technique to identify an...

Identifying Engineering Students' English Sentence Reading Comprehension Errors: Applying a Data Mining Technique

ERIC Educational Resources Information Center

Tsai, Yea-Ru; Ouyang, Chen-Sen; Chang, Yukon

2016-01-01

The purpose of this study is to propose a diagnostic approach to identify engineering students' English reading comprehension errors. Student data were collected during the process of reading texts of English for science and technology on a web-based cumulative sentence analysis system. For the analysis, the association-rule, data mining technique…

Dynamic association rules for gene expression data analysis.

PubMed

Chen, Shu-Chuan; Tsai, Tsung-Hsien; Chung, Cheng-Han; Li, Wen-Hsiung

2015-10-14

The purpose of gene expression analysis is to look for the association between regulation of gene expression levels and phenotypic variations. This association based on gene expression profile has been used to determine whether the induction/repression of genes correspond to phenotypic variations including cell regulations, clinical diagnoses and drug development. Statistical analyses on microarray data have been developed to resolve gene selection issue. However, these methods do not inform us of causality between genes and phenotypes. In this paper, we propose the dynamic association rule algorithm (DAR algorithm) which helps ones to efficiently select a subset of significant genes for subsequent analysis. The DAR algorithm is based on association rules from market basket analysis in marketing. We first propose a statistical way, based on constructing a one-sided confidence interval and hypothesis testing, to determine if an association rule is meaningful. Based on the proposed statistical method, we then developed the DAR algorithm for gene expression data analysis. The method was applied to analyze four microarray datasets and one Next Generation Sequencing (NGS) dataset: the Mice Apo A1 dataset, the whole genome expression dataset of mouse embryonic stem cells, expression profiling of the bone marrow of Leukemia patients, Microarray Quality Control (MAQC) data set and the RNA-seq dataset of a mouse genomic imprinting study. A comparison of the proposed method with the t-test on the expression profiling of the bone marrow of Leukemia patients was conducted. We developed a statistical way, based on the concept of confidence interval, to determine the minimum support and minimum confidence for mining association relationships among items. With the minimum support and minimum confidence, one can find significant rules in one single step. The DAR algorithm was then developed for gene expression data analysis. Four gene expression datasets showed that the proposed DAR algorithm not only was able to identify a set of differentially expressed genes that largely agreed with that of other methods, but also provided an efficient and accurate way to find influential genes of a disease. In the paper, the well-established association rule mining technique from marketing has been successfully modified to determine the minimum support and minimum confidence based on the concept of confidence interval and hypothesis testing. It can be applied to gene expression data to mine significant association rules between gene regulation and phenotype. The proposed DAR algorithm provides an efficient way to find influential genes that underlie the phenotypic variance.

iADRs: towards online adverse drug reaction analysis.

PubMed

Lin, Wen-Yang; Li, He-Yi; Du, Jhih-Wei; Feng, Wen-Yu; Lo, Chiao-Feng; Soo, Von-Wun

2012-12-01

Adverse Drug Reaction (ADR) is one of the most important issues in the assessment of drug safety. In fact, many adverse drug reactions are not discovered during limited pre-marketing clinical trials; instead, they are only observed after long term post-marketing surveillance of drug usage. In light of this, the detection of adverse drug reactions, as early as possible, is an important topic of research for the pharmaceutical industry. Recently, large numbers of adverse events and the development of data mining technology have motivated the development of statistical and data mining methods for the detection of ADRs. These stand-alone methods, with no integration into knowledge discovery systems, are tedious and inconvenient for users and the processes for exploration are time-consuming. This paper proposes an interactive system platform for the detection of ADRs. By integrating an ADR data warehouse and innovative data mining techniques, the proposed system not only supports OLAP style multidimensional analysis of ADRs, but also allows the interactive discovery of associations between drugs and symptoms, called a drug-ADR association rule, which can be further developed using other factors of interest to the user, such as demographic information. The experiments indicate that interesting and valuable drug-ADR association rules can be efficiently mined.

Association algorithm to mine the rules that govern enzyme definition and to classify protein sequences.

PubMed

Chiu, Shih-Hau; Chen, Chien-Chi; Yuan, Gwo-Fang; Lin, Thy-Hou

2006-06-15

The number of sequences compiled in many genome projects is growing exponentially, but most of them have not been characterized experimentally. An automatic annotation scheme must be in an urgent need to reduce the gap between the amount of new sequences produced and reliable functional annotation. This work proposes rules for automatically classifying the fungus genes. The approach involves elucidating the enzyme classifying rule that is hidden in UniProt protein knowledgebase and then applying it for classification. The association algorithm, Apriori, is utilized to mine the relationship between the enzyme class and significant InterPro entries. The candidate rules are evaluated for their classificatory capacity. There were five datasets collected from the Swiss-Prot for establishing the annotation rules. These were treated as the training sets. The TrEMBL entries were treated as the testing set. A correct enzyme classification rate of 70% was obtained for the prokaryote datasets and a similar rate of about 80% was obtained for the eukaryote datasets. The fungus training dataset which lacks an enzyme class description was also used to evaluate the fungus candidate rules. A total of 88 out of 5085 test entries were matched with the fungus rule set. These were otherwise poorly annotated using their functional descriptions. The feasibility of using the method presented here to classify enzyme classes based on the enzyme domain rules is evident. The rules may be also employed by the protein annotators in manual annotation or implemented in an automatic annotation flowchart.

Efficient mining of association rules for the early diagnosis of Alzheimer's disease

NASA Astrophysics Data System (ADS)

Chaves, R.; Górriz, J. M.; Ramírez, J.; Illán, I. A.; Salas-Gonzalez, D.; Gómez-Río, M.

2011-09-01

In this paper, a novel technique based on association rules (ARs) is presented in order to find relations among activated brain areas in single photon emission computed tomography (SPECT) imaging. In this sense, the aim of this work is to discover associations among attributes which characterize the perfusion patterns of normal subjects and to make use of them for the early diagnosis of Alzheimer's disease (AD). Firstly, voxel-as-feature-based activation estimation methods are used to find the tridimensional activated brain regions of interest (ROIs) for each patient. These ROIs serve as input to secondly mine ARs with a minimum support and confidence among activation blocks by using a set of controls. In this context, support and confidence measures are related to the proportion of functional areas which are singularly and mutually activated across the brain. Finally, we perform image classification by comparing the number of ARs verified by each subject under test to a given threshold that depends on the number of previously mined rules. Several classification experiments were carried out in order to evaluate the proposed methods using a SPECT database that consists of 41 controls (NOR) and 56 AD patients labeled by trained physicians. The proposed methods were validated by means of the leave-one-out cross validation strategy, yielding up to 94.87% classification accuracy, thus outperforming recent developed methods for computer aided diagnosis of AD.

Mining Context-Aware Association Rules Using Grammar-Based Genetic Programming.

PubMed

Luna, Jose Maria; Pechenizkiy, Mykola; Del Jesus, Maria Jose; Ventura, Sebastian

2017-09-25

Real-world data usually comprise features whose interpretation depends on some contextual information. Such contextual-sensitive features and patterns are of high interest to be discovered and analyzed in order to obtain the right meaning. This paper formulates the problem of mining context-aware association rules, which refers to the search for associations between itemsets such that the strength of their implication depends on a contextual feature. For the discovery of this type of associations, a model that restricts the search space and includes syntax constraints by means of a grammar-based genetic programming methodology is proposed. Grammars can be considered as a useful way of introducing subjective knowledge to the pattern mining process as they are highly related to the background knowledge of the user. The performance and usefulness of the proposed approach is examined by considering synthetically generated datasets. A posteriori analysis on different domains is also carried out to demonstrate the utility of this kind of associations. For example, in educational domains, it is essential to identify and understand contextual and context-sensitive factors that affect overall and individual student behavior and performance. The results of the experiments suggest that the approach is feasible and it automatically identifies interesting context-aware associations from real-world datasets.

Mining Student Data Captured from a Web-Based Tutoring Tool: Initial Exploration and Results

ERIC Educational Resources Information Center

Merceron, Agathe; Yacef, Kalina

2004-01-01

In this article we describe the initial investigations that we have conducted on student data collected from a web-based tutoring tool. We have used some data mining techniques such as association rule and symbolic data analysis, as well as traditional SQL queries to gain further insight on the students' learning and deduce information to improve…

The Application of Data Mining Techniques to Create Promotion Strategy for Mobile Phone Shop

NASA Astrophysics Data System (ADS)

Khasanah, A. U.; Wibowo, K. S.; Dewantoro, H. F.

2017-12-01

The number of mobile shop is growing very fast in various regions in Indonesia including in Yogyakarta due to the increasing demand of mobile phone. This fact leads high competition among the mobile phone shops. In these conditions the mobile phone shop should have a good promotion strategy in order to survive in competition, especially for a small mobile phone shop. To create attractive promotion strategy, the companies/shops should know their customer segmentation and the buying pattern of their target market. These kind of analysis can be done using Data mining technique. This study aims to segment customer using Agglomerative Hierarchical Clustering and know customer buying pattern using Association Rule Mining. This result conducted in a mobile shop in Sleman Yogyakarta. The clustering result shows that the biggest customer segment of the shop was male university student who come on weekend and from association rule mining, it can be concluded that tempered glass and smart phone “x” as well as action camera and waterproof monopod and power bank have strong relationship. This results that used to create promotion strategies which are presented in the end of the study.

Quantum algorithm for association rules mining

NASA Astrophysics Data System (ADS)

Yu, Chao-Hua; Gao, Fei; Wang, Qing-Le; Wen, Qiao-Yan

2016-10-01

Association rules mining (ARM) is one of the most important problems in knowledge discovery and data mining. Given a transaction database that has a large number of transactions and items, the task of ARM is to acquire consumption habits of customers by discovering the relationships between itemsets (sets of items). In this paper, we address ARM in the quantum settings and propose a quantum algorithm for the key part of ARM, finding frequent itemsets from the candidate itemsets and acquiring their supports. Specifically, for the case in which there are Mf(k ) frequent k -itemsets in the Mc(k ) candidate k -itemsets (Mf(k )≤Mc(k ) ), our algorithm can efficiently mine these frequent k -itemsets and estimate their supports by using parallel amplitude estimation and amplitude amplification with complexity O (k/√{Mc(k )Mf(k ) } ɛ ) , where ɛ is the error for estimating the supports. Compared with the classical counterpart, i.e., the classical sampling-based algorithm, whose complexity is O (k/Mc(k ) ɛ2) , our quantum algorithm quadratically improves the dependence on both ɛ and Mc(k ) in the best case when Mf(k )≪Mc(k ) and on ɛ alone in the worst case when Mf(k )≈Mc(k ) .

[Research of bleeding volume and method in blood-letting acupuncture therapy based on data mining].

PubMed

Liu, Xin; Jia, Chun-Sheng; Wang, Jian-Ling; Du, Yu-Zhu; Zhang, Xiao-Xu; Shi, Jing; Li, Xiao-Feng; Sun, Yan-Hui; Zhang, Shen; Zhang, Xuan-Ping; Gang, Wei-Juan

2014-03-01

Through computer-based technology and data mining method, with treatment in cases of bloodletting acupuncture therapy in collected literature as sample data, the association rule in data mining was applied. According to self-built database platform, the data was input, arranged and summarized, and eventually required data was acquired to perform the data mining of bleeding volume and method in blood-letting acupuncture therapy, which summarized its application rules and clinical values to provide better guide for clinical practice. There were 9 kinds of blood-letting tools in the literature, in which the frequency of three-edge needle was the highest, accounting for 84.4% (1239/1468). The bleeding volume was classified into six levels, in which less volume (less than 0.1 mL) had the highest frequency (401 times). According to the results of the data mining, blood-letting acupuncture therapy was widely applied in clinical practice of acupuncture, in which use of three-edge needle and less volume (less than 0.1 mL) of blood were the most common, however, there was no central tendency in general.

76 FR 2617 - Lowering Miners' Exposure to Respirable Coal Mine Dust, Including Continuous Personal Dust Monitors

Federal Register 2010, 2011, 2012, 2013, 2014

2011-01-14

... 1219-AB64 Lowering Miners' Exposure to Respirable Coal Mine Dust, Including Continuous Personal Dust... comment period on the proposed rule addressing Lowering Miners' Exposure to Respirable Coal Mine Dust...), MSHA published a proposed rule, Lowering Miners' Exposure to Respirable Coal Mine Dust, Including...

77 FR 34894 - Wyoming Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-12

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 950... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; withdrawal. SUMMARY: We, the Office of Surface Mining Reclamation and Enforcement (OSM), are announcing the withdrawal of a proposed rule...

Associations between socio-demographic characteristics and chemical concentrations contributing to cumulative exposures in the United States

EPA Science Inventory

Association rule mining (ARM) has been widely used to identify associations between various entities in many fields. Although some studies have utilized it to analyze the relationship between chemicals and human health effects, fewer have used this technique to identify and quant...

Analyzing Large Gene Expression and Methylation Data Profiles Using StatBicRM: Statistical Biclustering-Based Rule Mining

PubMed Central

Maulik, Ujjwal; Mallik, Saurav; Mukhopadhyay, Anirban; Bandyopadhyay, Sanghamitra

2015-01-01

Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples. In this article, we propose a computational rule mining framework, StatBicRM (i.e., statistical biclustering-based rule mining) to identify special type of rules and potential biomarkers using integrated approaches of statistical and binary inclusion-maximal biclustering techniques from the biological datasets. At first, a novel statistical strategy has been utilized to eliminate the insignificant/low-significant/redundant genes in such way that significance level must satisfy the data distribution property (viz., either normal distribution or non-normal distribution). The data is then discretized and post-discretized, consecutively. Thereafter, the biclustering technique is applied to identify maximal frequent closed homogeneous itemsets. Corresponding special type of rules are then extracted from the selected itemsets. Our proposed rule mining method performs better than the other rule mining algorithms as it generates maximal frequent closed homogeneous itemsets instead of frequent itemsets. Thus, it saves elapsed time, and can work on big dataset. Pathway and Gene Ontology analyses are conducted on the genes of the evolved rules using David database. Frequency analysis of the genes appearing in the evolved rules is performed to determine potential biomarkers. Furthermore, we also classify the data to know how much the evolved rules are able to describe accurately the remaining test (unknown) data. Subsequently, we also compare the average classification accuracy, and other related factors with other rule-based classifiers. Statistical significance tests are also performed for verifying the statistical relevance of the comparative results. Here, each of the other rule mining methods or rule-based classifiers is also starting with the same post-discretized data-matrix. Finally, we have also included the integrated analysis of gene expression and methylation for determining epigenetic effect (viz., effect of methylation) on gene expression level. PMID:25830807

Analyzing large gene expression and methylation data profiles using StatBicRM: statistical biclustering-based rule mining.

PubMed

Maulik, Ujjwal; Mallik, Saurav; Mukhopadhyay, Anirban; Bandyopadhyay, Sanghamitra

2015-01-01

Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples. In this article, we propose a computational rule mining framework, StatBicRM (i.e., statistical biclustering-based rule mining) to identify special type of rules and potential biomarkers using integrated approaches of statistical and binary inclusion-maximal biclustering techniques from the biological datasets. At first, a novel statistical strategy has been utilized to eliminate the insignificant/low-significant/redundant genes in such way that significance level must satisfy the data distribution property (viz., either normal distribution or non-normal distribution). The data is then discretized and post-discretized, consecutively. Thereafter, the biclustering technique is applied to identify maximal frequent closed homogeneous itemsets. Corresponding special type of rules are then extracted from the selected itemsets. Our proposed rule mining method performs better than the other rule mining algorithms as it generates maximal frequent closed homogeneous itemsets instead of frequent itemsets. Thus, it saves elapsed time, and can work on big dataset. Pathway and Gene Ontology analyses are conducted on the genes of the evolved rules using David database. Frequency analysis of the genes appearing in the evolved rules is performed to determine potential biomarkers. Furthermore, we also classify the data to know how much the evolved rules are able to describe accurately the remaining test (unknown) data. Subsequently, we also compare the average classification accuracy, and other related factors with other rule-based classifiers. Statistical significance tests are also performed for verifying the statistical relevance of the comparative results. Here, each of the other rule mining methods or rule-based classifiers is also starting with the same post-discretized data-matrix. Finally, we have also included the integrated analysis of gene expression and methylation for determining epigenetic effect (viz., effect of methylation) on gene expression level.

Using GO-WAR for mining cross-ontology weighted association rules.

PubMed

Agapito, Giuseppe; Cannataro, Mario; Guzzi, Pietro Hiram; Milano, Marianna

2015-07-01

The Gene Ontology (GO) is a structured repository of concepts (GO terms) that are associated to one or more gene products. The process of association is referred to as annotation. The relevance and the specificity of both GO terms and annotations are evaluated by a measure defined as information content (IC). The analysis of annotated data is thus an important challenge for bioinformatics. There exist different approaches of analysis. From those, the use of association rules (AR) may provide useful knowledge, and it has been used in some applications, e.g. improving the quality of annotations. Nevertheless classical association rules algorithms do not take into account the source of annotation nor the importance yielding to the generation of candidate rules with low IC. This paper presents GO-WAR (Gene Ontology-based Weighted Association Rules) a methodology for extracting weighted association rules. GO-WAR can extract association rules with a high level of IC without loss of support and confidence from a dataset of annotated data. A case study on using of GO-WAR on publicly available GO annotation datasets is used to demonstrate that our method outperforms current state of the art approaches. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

An Analysis Pipeline with Statistical and Visualization-Guided Knowledge Discovery for Michigan-Style Learning Classifier Systems

PubMed Central

Urbanowicz, Ryan J.; Granizo-Mackenzie, Ambrose; Moore, Jason H.

2014-01-01

Michigan-style learning classifier systems (M-LCSs) represent an adaptive and powerful class of evolutionary algorithms which distribute the learned solution over a sizable population of rules. However their application to complex real world data mining problems, such as genetic association studies, has been limited. Traditional knowledge discovery strategies for M-LCS rule populations involve sorting and manual rule inspection. While this approach may be sufficient for simpler problems, the confounding influence of noise and the need to discriminate between predictive and non-predictive attributes calls for additional strategies. Additionally, tests of significance must be adapted to M-LCS analyses in order to make them a viable option within fields that require such analyses to assess confidence. In this work we introduce an M-LCS analysis pipeline that combines uniquely applied visualizations with objective statistical evaluation for the identification of predictive attributes, and reliable rule generalizations in noisy single-step data mining problems. This work considers an alternative paradigm for knowledge discovery in M-LCSs, shifting the focus from individual rules to a global, population-wide perspective. We demonstrate the efficacy of this pipeline applied to the identification of epistasis (i.e., attribute interaction) and heterogeneity in noisy simulated genetic association data. PMID:25431544

Association algorithm to mine the rules that govern enzyme definition and to classify protein sequences

PubMed Central

Chiu, Shih-Hau; Chen, Chien-Chi; Yuan, Gwo-Fang; Lin, Thy-Hou

2006-01-01

Background The number of sequences compiled in many genome projects is growing exponentially, but most of them have not been characterized experimentally. An automatic annotation scheme must be in an urgent need to reduce the gap between the amount of new sequences produced and reliable functional annotation. This work proposes rules for automatically classifying the fungus genes. The approach involves elucidating the enzyme classifying rule that is hidden in UniProt protein knowledgebase and then applying it for classification. The association algorithm, Apriori, is utilized to mine the relationship between the enzyme class and significant InterPro entries. The candidate rules are evaluated for their classificatory capacity. Results There were five datasets collected from the Swiss-Prot for establishing the annotation rules. These were treated as the training sets. The TrEMBL entries were treated as the testing set. A correct enzyme classification rate of 70% was obtained for the prokaryote datasets and a similar rate of about 80% was obtained for the eukaryote datasets. The fungus training dataset which lacks an enzyme class description was also used to evaluate the fungus candidate rules. A total of 88 out of 5085 test entries were matched with the fungus rule set. These were otherwise poorly annotated using their functional descriptions. Conclusion The feasibility of using the method presented here to classify enzyme classes based on the enzyme domain rules is evident. The rules may be also employed by the protein annotators in manual annotation or implemented in an automatic annotation flowchart. PMID:16776838

[The method and application to construct experience recommendation platform of acupuncture ancient books based on data mining technology].

PubMed

Chen, Chuyun; Hong, Jiaming; Zhou, Weilin; Lin, Guohua; Wang, Zhengfei; Zhang, Qufei; Lu, Cuina; Lu, Lihong

2017-07-12

To construct a knowledge platform of acupuncture ancient books based on data mining technology, and to provide retrieval service for users. The Oracle 10 g database was applied and JAVA was selected as development language; based on the standard library and ancient books database established by manual entry, a variety of data mining technologies, including word segmentation, speech tagging, dependency analysis, rule extraction, similarity calculation, ambiguity analysis, supervised classification technology were applied to achieve text automatic extraction of ancient books; in the last, through association mining and decision analysis, the comprehensive and intelligent analysis of disease and symptom, meridians, acupoints, rules of acupuncture and moxibustion in acupuncture ancient books were realized, and retrieval service was provided for users through structure of browser/server (B/S). The platform realized full-text retrieval, word frequency analysis and association analysis; when diseases or acupoints were searched, the frequencies of meridian, acupoints (diseases) and techniques were presented from high to low, meanwhile the support degree and confidence coefficient between disease and acupoints (special acupoint), acupoints and acupoints in prescription, disease or acupoints and technique were presented. The experience platform of acupuncture ancient books based on data mining technology could be used as a reference for selection of disease, meridian and acupoint in clinical treatment and education of acupuncture and moxibustion.

PubMedMiner: Mining and Visualizing MeSH-based Associations in PubMed.

PubMed

Zhang, Yucan; Sarkar, Indra Neil; Chen, Elizabeth S

2014-01-01

The exponential growth of biomedical literature provides the opportunity to develop approaches for facilitating the identification of possible relationships between biomedical concepts. Indexing by Medical Subject Headings (MeSH) represent high-quality summaries of much of this literature that can be used to support hypothesis generation and knowledge discovery tasks using techniques such as association rule mining. Based on a survey of literature mining tools, a tool implemented using Ruby and R - PubMedMiner - was developed in this study for mining and visualizing MeSH-based associations for a set of MEDLINE articles. To demonstrate PubMedMiner's functionality, a case study was conducted that focused on identifying and comparing comorbidities for asthma in children and adults. Relative to the tools surveyed, the initial results suggest that PubMedMiner provides complementary functionality for summarizing and comparing topics as well as identifying potentially new knowledge.

Exploring associations of clinical and social parameters with violent behaviors among psychiatric patients.

PubMed

Dai, Hong-Jie; Su, Emily Chia-Yu; Uddin, Mohy; Jonnagaddala, Jitendra; Wu, Chi-Shin; Syed-Abdul, Shabbir

2017-11-01

Evidence has revealed interesting associations of clinical and social parameters with violent behaviors of patients with psychiatric disorders. Men are more violent preceding and during hospitalization, whereas women are more violent than men throughout the 3days following a hospital admission. It has also been proven that mental disorders may be a consistent risk factor for the occurrence of violence. In order to better understand violent behaviors of patients with psychiatric disorders, it is important to investigate both the clinical symptoms and psychosocial factors that accompany violence in these patients. In this study, we utilized a dataset released by the Partners Healthcare and Neuropsychiatric Genome-scale and RDoC Individualized Domains project of Harvard Medical School to develop a unique text mining pipeline that processes unstructured clinical data in order to recognize clinical and social parameters such asage, gender, history of alcohol use, and violent behaviors, and explored the associations between these parameters and violent behaviors of patients with psychiatric disorders. The aim of our work was to demonstrate the feasibility of mining factors that are strongly associated with violent behaviors among psychiatric patients from unstructured psychiatric evaluation records using clinical text mining. Experiment results showed that stimulants, followed by a family history of violent behavior, suicidal behaviors, and financial stress were strongly associated with violent behaviors. Key aspects explicated in this paper include employing our text mining pipeline to extract clinical and social factors linked with violent behaviors, generating association rules to uncover possible associations between these factors and violent behaviors, and lastly the ranking of top rules associated with violent behaviors using statistical analysis and interpretation. Copyright © 2017. Published by Elsevier Inc.

75 FR 22723 - Stream Protection Rule; Environmental Impact Statement

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-30

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Parts 780... of Surface Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; notice of intent to prepare an environmental impact statement. SUMMARY: We, the Office of Surface Mining Reclamation and...

A novel association rule mining approach using TID intermediate itemset.

PubMed

Aqra, Iyad; Herawan, Tutut; Abdul Ghani, Norjihan; Akhunzada, Adnan; Ali, Akhtar; Bin Razali, Ramdan; Ilahi, Manzoor; Raymond Choo, Kim-Kwang

2018-01-01

Designing an efficient association rule mining (ARM) algorithm for multilevel knowledge-based transactional databases that is appropriate for real-world deployments is of paramount concern. However, dynamic decision making that needs to modify the threshold either to minimize or maximize the output knowledge certainly necessitates the extant state-of-the-art algorithms to rescan the entire database. Subsequently, the process incurs heavy computation cost and is not feasible for real-time applications. The paper addresses efficiently the problem of threshold dynamic updation for a given purpose. The paper contributes by presenting a novel ARM approach that creates an intermediate itemset and applies a threshold to extract categorical frequent itemsets with diverse threshold values. Thus, improving the overall efficiency as we no longer needs to scan the whole database. After the entire itemset is built, we are able to obtain real support without the need of rebuilding the itemset (e.g. Itemset list is intersected to obtain the actual support). Moreover, the algorithm supports to extract many frequent itemsets according to a pre-determined minimum support with an independent purpose. Additionally, the experimental results of our proposed approach demonstrate the capability to be deployed in any mining system in a fully parallel mode; consequently, increasing the efficiency of the real-time association rules discovery process. The proposed approach outperforms the extant state-of-the-art and shows promising results that reduce computation cost, increase accuracy, and produce all possible itemsets.

A novel association rule mining approach using TID intermediate itemset

PubMed Central

Ali, Akhtar; Bin Razali, Ramdan; Ilahi, Manzoor; Raymond Choo, Kim-Kwang

2018-01-01

Designing an efficient association rule mining (ARM) algorithm for multilevel knowledge-based transactional databases that is appropriate for real-world deployments is of paramount concern. However, dynamic decision making that needs to modify the threshold either to minimize or maximize the output knowledge certainly necessitates the extant state-of-the-art algorithms to rescan the entire database. Subsequently, the process incurs heavy computation cost and is not feasible for real-time applications. The paper addresses efficiently the problem of threshold dynamic updation for a given purpose. The paper contributes by presenting a novel ARM approach that creates an intermediate itemset and applies a threshold to extract categorical frequent itemsets with diverse threshold values. Thus, improving the overall efficiency as we no longer needs to scan the whole database. After the entire itemset is built, we are able to obtain real support without the need of rebuilding the itemset (e.g. Itemset list is intersected to obtain the actual support). Moreover, the algorithm supports to extract many frequent itemsets according to a pre-determined minimum support with an independent purpose. Additionally, the experimental results of our proposed approach demonstrate the capability to be deployed in any mining system in a fully parallel mode; consequently, increasing the efficiency of the real-time association rules discovery process. The proposed approach outperforms the extant state-of-the-art and shows promising results that reduce computation cost, increase accuracy, and produce all possible itemsets. PMID:29351287

Application of text mining for customer evaluations in commercial banking

NASA Astrophysics Data System (ADS)

Tan, Jing; Du, Xiaojiang; Hao, Pengpeng; Wang, Yanbo J.

2015-07-01

Nowadays customer attrition is increasingly serious in commercial banks. To combat this problem roundly, mining customer evaluation texts is as important as mining customer structured data. In order to extract hidden information from customer evaluations, Textual Feature Selection, Classification and Association Rule Mining are necessary techniques. This paper presents all three techniques by using Chinese Word Segmentation, C5.0 and Apriori, and a set of experiments were run based on a collection of real textual data that includes 823 customer evaluations taken from a Chinese commercial bank. Results, consequent solutions, some advice for the commercial bank are given in this paper.

Investigating the Relation Between Prevalence of Asthmatic Allergy with the Characteristics of the Environment Using Association Rule Mining

NASA Astrophysics Data System (ADS)

Kanani Sadat, Y.; Karimipour, F.; Kanani Sadat, A.

2014-10-01

The prevalence of allergic diseases has highly increased in recent decades due to contamination of the environment with the allergy stimuli. A common treat is identifying the allergy stimulus and, then, avoiding the patient to be exposed with it. There are, however, many unknown allergic diseases stimuli that are related to the characteristics of the living environment. In this paper, we focus on the effect of air pollution on asthmatic allergies and investigate the association between prevalence of such allergies with those characteristics of the environment that may affect the air pollution. For this, spatial association rule mining has been deployed to mine the association between spatial distribution of allergy prevalence and the air pollution parameters such as CO, SO2, NO2, PM10, PM2.5, and O3 (compiled by the air pollution monitoring stations) as well as living distance to parks and roads. The results for the case study (i.e., Tehran metropolitan area) indicates that distance to parks and roads as well as CO, NO2, PM10, and PM2.5 is related to the allergy prevalence in December (the most polluted month of the year in Tehran), while SO2 and O3 have no effect on that.

Personalized Privacy-Preserving Frequent Itemset Mining Using Randomized Response

PubMed Central

Sun, Chongjing; Fu, Yan; Zhou, Junlin; Gao, Hui

2014-01-01

Frequent itemset mining is the important first step of association rule mining, which discovers interesting patterns from the massive data. There are increasing concerns about the privacy problem in the frequent itemset mining. Some works have been proposed to handle this kind of problem. In this paper, we introduce a personalized privacy problem, in which different attributes may need different privacy levels protection. To solve this problem, we give a personalized privacy-preserving method by using the randomized response technique. By providing different privacy levels for different attributes, this method can get a higher accuracy on frequent itemset mining than the traditional method providing the same privacy level. Finally, our experimental results show that our method can have better results on the frequent itemset mining while preserving personalized privacy. PMID:25143989

Personalized privacy-preserving frequent itemset mining using randomized response.

PubMed

Sun, Chongjing; Fu, Yan; Zhou, Junlin; Gao, Hui

2014-01-01

Frequent itemset mining is the important first step of association rule mining, which discovers interesting patterns from the massive data. There are increasing concerns about the privacy problem in the frequent itemset mining. Some works have been proposed to handle this kind of problem. In this paper, we introduce a personalized privacy problem, in which different attributes may need different privacy levels protection. To solve this problem, we give a personalized privacy-preserving method by using the randomized response technique. By providing different privacy levels for different attributes, this method can get a higher accuracy on frequent itemset mining than the traditional method providing the same privacy level. Finally, our experimental results show that our method can have better results on the frequent itemset mining while preserving personalized privacy.

A New Data Mining Scheme Using Artificial Neural Networks

PubMed Central

Kamruzzaman, S. M.; Jehad Sarkar, A. M.

2011-01-01

Classification is one of the data mining problems receiving enormous attention in the database community. Although artificial neural networks (ANNs) have been successfully applied in a wide range of machine learning applications, they are however often regarded as black boxes, i.e., their predictions cannot be explained. To enhance the explanation of ANNs, a novel algorithm to extract symbolic rules from ANNs has been proposed in this paper. ANN methods have not been effectively utilized for data mining tasks because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or interpretation by human experts. With the proposed approach, concise symbolic rules with high accuracy, that are easily explainable, can be extracted from the trained ANNs. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and the accuracy. The effectiveness of the proposed approach is clearly demonstrated by the experimental results on a set of benchmark data mining classification problems. PMID:22163866

Data Mining for Financial Applications

NASA Astrophysics Data System (ADS)

Kovalerchuk, Boris; Vityaev, Evgenii

This chapter describes Data Mining in finance by discussing financial tasks, specifics of methodologies and techniques in this Data Mining area. It includes time dependence, data selection, forecast horizon, measures of success, quality of patterns, hypothesis evaluation, problem ID, method profile, attribute-based and relational methodologies. The second part of the chapter discusses Data Mining models and practice in finance. It covers use of neural networks in portfolio management, design of interpretable trading rules and discovering money laundering schemes using decision rules and relational Data Mining methodology.

Analysis 320 coal mine accidents using structural equation modeling with unsafe conditions of the rules and regulations as exogenous variables.

PubMed

Zhang, Yingyu; Shao, Wei; Zhang, Mengjia; Li, Hejun; Yin, Shijiu; Xu, Yingjun

2016-07-01

Mining has been historically considered as a naturally high-risk industry worldwide. Deaths caused by coal mine accidents are more than the sum of all other accidents in China. Statistics of 320 coal mine accidents in Shandong province show that all accidents contain indicators of "unsafe conditions of the rules and regulations" with a frequency of 1590, accounting for 74.3% of the total frequency of 2140. "Unsafe behaviors of the operator" is another important contributory factor, which mainly includes "operator error" and "venturing into dangerous places." A systems analysis approach was applied by using structural equation modeling (SEM) to examine the interactions between the contributory factors of coal mine accidents. The analysis of results leads to three conclusions. (i) "Unsafe conditions of the rules and regulations," affect the "unsafe behaviors of the operator," "unsafe conditions of the equipment," and "unsafe conditions of the environment." (ii) The three influencing factors of coal mine accidents (with the frequency of effect relation in descending order) are "lack of safety education and training," "rules and regulations of safety production responsibility," and "rules and regulations of supervision and inspection." (iii) The three influenced factors (with the frequency in descending order) of coal mine accidents are "venturing into dangerous places," "poor workplace environment," and "operator error." Copyright © 2016 Elsevier Ltd. All rights reserved.

Highly scalable and robust rule learner: performance evaluation and comparison.

PubMed

Kurgan, Lukasz A; Cios, Krzysztof J; Dick, Scott

2006-02-01

Business intelligence and bioinformatics applications increasingly require the mining of datasets consisting of millions of data points, or crafting real-time enterprise-level decision support systems for large corporations and drug companies. In all cases, there needs to be an underlying data mining system, and this mining system must be highly scalable. To this end, we describe a new rule learner called DataSqueezer. The learner belongs to the family of inductive supervised rule extraction algorithms. DataSqueezer is a simple, greedy, rule builder that generates a set of production rules from labeled input data. In spite of its relative simplicity, DataSqueezer is a very effective learner. The rules generated by the algorithm are compact, comprehensible, and have accuracy comparable to rules generated by other state-of-the-art rule extraction algorithms. The main advantages of DataSqueezer are very high efficiency, and missing data resistance. DataSqueezer exhibits log-linear asymptotic complexity with the number of training examples, and it is faster than other state-of-the-art rule learners. The learner is also robust to large quantities of missing data, as verified by extensive experimental comparison with the other learners. DataSqueezer is thus well suited to modern data mining and business intelligence tasks, which commonly involve huge datasets with a large fraction of missing data.

5 CFR 5201.105 - Additional rules for Mine Safety and Health Administration employees.

Code of Federal Regulations, 2010 CFR

2010-01-01

... Health Administration employees. 5201.105 Section 5201.105 Administrative Personnel DEPARTMENT OF LABOR... for Mine Safety and Health Administration employees. The rules in this section apply to employees of the Mine Safety and Health Administration (MSHA) and are in addition to §§ 5201.101, 5201.102, and...

From data mining rules to medical logical modules and medical advices.

PubMed

Gomoi, Valentin; Vida, Mihaela; Robu, Raul; Stoicu-Tivadar, Vasile; Bernad, Elena; Lupşe, Oana

2013-01-01

Using data mining in collaboration with Clinical Decision Support Systems adds new knowledge as support for medical diagnosis. The current work presents a tool which translates data mining rules supporting generation of medical advices to Arden Syntax formalism. The developed system was tested with data related to 2326 births that took place in 2010 at the Bega Obstetrics - Gynaecology Hospital, Timişoara. Based on processing these data, 14 medical rules regarding the Apgar score were generated and then translated in Arden Syntax language.

Medication regularity of pulmonary fibrosis treatment by contemporary traditional Chinese medicine experts based on data mining.

PubMed

Zhang, Suxian; Wu, Hao; Liu, Jie; Gu, Huihui; Li, Xiujuan; Zhang, Tiansong

2018-03-01

Treatment of pulmonary fibrosis by traditional Chinese medicine (TCM) has accumulated important experience. Our interest is in exploring the medication regularity of contemporary Chinese medical specialists treating pulmonary fibrosis. Through literature search, medical records from TCM experts who treat pulmonary fibrosis, which were published in Chinese and English medical journals, were selected for this study. As the object of study, a database was established after analysing the records. After data cleaning, the rules of medicine in the treatment of pulmonary fibrosis in medical records of TCM were explored by using data mining technologies such as frequency analysis, association rule analysis, and link analysis. A total of 124 medical records from 60 doctors were selected in this study; 263 types of medicinals were used a total of 5,455 times; the herbs that were used more than 30 times can be grouped into 53 species and were used a total of 3,681 times. Using main medicinals cluster analysis, medicinals were divided into qi-tonifying, yin-tonifying, blood-activating, phlegm-resolving, cough-suppressing, panting-calming, and ten other major medicinal categories. According to the set conditions, a total of 62 drug compatibility rules have been obtained, involving mainly qi-tonifying, yin-tonifying, blood-activating, phlegm-resolving, qi-descending, and panting-calming medicinals, as well as other medicinals used in combination. The results of data mining are consistent with clinical practice and it is feasible to explore the medical rules applicable to the treatment of pulmonary fibrosis in medical records of TCM by data mining.

Visualization of usability and functionality of a professional website through web-mining.

PubMed

Jones, Josette F; Mahoui, Malika; Gopa, Venkata Devi Pragna

2007-10-11

Functional interface design requires understanding of the information system structure and the user. Web logs record user interactions with the interface, and thus provide some insight into user search behavior and efficiency of the search process. The present study uses a data-mining approach with techniques such as association rules, clustering and classification, to visualize the usability and functionality of a digital library through in depth analyses of web logs.

Association Rule Analysis for Tour Route Recommendation and Application to Wctsnop

NASA Astrophysics Data System (ADS)

Fang, H.; Chen, C.; Lin, J.; Liu, X.; Fang, D.

2017-09-01

The increasing E-tourism systems provide intelligent tour recommendation for tourists. In this sense, recommender system can make personalized suggestions and provide satisfied information associated with their tour cycle. Data mining is a proper tool that extracting potential information from large database for making strategic decisions. In the study, association rule analysis based on FP-growth algorithm is applied to find the association relationship among scenic spots in different cities as tour route recommendation. In order to figure out valuable rules, Kulczynski interestingness measure is adopted and imbalance ratio is computed. The proposed scheme was evaluated on Wangluzhe cultural tourism service network operation platform (WCTSNOP), where it could verify that it is able to quick recommend tour route and to rapidly enhance the recommendation quality.

In Brief: Coal mining regulations

NASA Astrophysics Data System (ADS)

Showstack, Randy

2009-12-01

The U.S. Department of the Interior (DOI) announced on 18 November measures to strengthen the oversight of state surface coal mining programs and to promulgate federal regulations to protect streams affected by surface coal mining operations. DOI's Office of Surface Mining Reclamation and Enforcement (OSM) is publishing an advance notice of a proposed rule about protecting streams from adverse impacts of surface coal mining operations. A rule issued by the Bush administration in December 2008 allows coal mine operators to place excess excavated materials into streams if they can show it is not reasonably possible to avoid doing so. “We are moving as quickly as possible under the law to gather public input for a new rule, based on sound science, that will govern how companies handle fill removed from mountaintop coal seams,” according to Wilma Lewis, assistant secretary for Land and Minerals Management at DOI.

SCADA-based Operator Support System for Power Plant Equipment Fault Forecasting

NASA Astrophysics Data System (ADS)

Mayadevi, N.; Ushakumari, S. S.; Vinodchandra, S. S.

2014-12-01

Power plant equipment must be monitored closely to prevent failures from disrupting plant availability. Online monitoring technology integrated with hybrid forecasting techniques can be used to prevent plant equipment faults. A self learning rule-based expert system is proposed in this paper for fault forecasting in power plants controlled by supervisory control and data acquisition (SCADA) system. Self-learning utilizes associative data mining algorithms on the SCADA history database to form new rules that can dynamically update the knowledge base of the rule-based expert system. In this study, a number of popular associative learning algorithms are considered for rule formation. Data mining results show that the Tertius algorithm is best suited for developing a learning engine for power plants. For real-time monitoring of the plant condition, graphical models are constructed by K-means clustering. To build a time-series forecasting model, a multi layer preceptron (MLP) is used. Once created, the models are updated in the model library to provide an adaptive environment for the proposed system. Graphical user interface (GUI) illustrates the variation of all sensor values affecting a particular alarm/fault, as well as the step-by-step procedure for avoiding critical situations and consequent plant shutdown. The forecasting performance is evaluated by computing the mean absolute error and root mean square error of the predictions.

77 FR 43721 - Examinations of Work Areas in Underground Coal Mines for Violations of Mandatory Health or Safety...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-26

... Examinations of Work Areas in Underground Coal Mines for Violations of Mandatory Health or Safety Standards... effectiveness of information collection requirements contained in the final rule on Examinations of Work Areas... requirements in MSHA's final rule on Examinations of Work Areas in Underground Coal Mines for Violations of...

75 FR 28227 - National Emission Standards for Hazardous Air Pollutants: Gold Mine Ore Processing and Production...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-05-20

... published a proposed rule for mercury emissions from the gold mine ore processing and production area source... proposed rule (75 FR 22470). Several parties requested that EPA extend the comment period. EPA has granted...-AP48 National Emission Standards for Hazardous Air Pollutants: Gold Mine Ore Processing and Production...

Rules of meridians and acupoints selection in treatment of Parkinson's disease based on data mining techniques.

PubMed

Li, Zhe; Hu, Ying-Yu; Zheng, Chun-Ye; Su, Qiao-Zhen; An, Chang; Luo, Xiao-Dong; Liu, Mao-Cai

2018-01-15

To help selecting appropriate meridians and acupoints in clinical practice and experimental study for Parkinson's disease (PD), the rules of meridians and acupoints selection of acupuncture and moxibustion were analyzed in domestic and foreign clinical treatment for PD based on data mining techniques. Literature about PD treated by acupuncture and moxibustion in China and abroad was searched and selected from China National Knowledge Infrastructure and MEDLINE. Then the data from all eligible articles were extracted to establish the database of acupuncture-moxibustion for PD. The association rules of data mining techniques were used to analyze the rules of meridians and acupoints selection. Totally, 168 eligible articles were included and 184 acupoints were applied. The total frequency of acupoints application was 1,090 times. Those acupoints were mainly distributed in head and neck and extremities. Among all, Taichong (LR 3), Baihui (DU 20), Fengchi (GB 20), Hegu (LI 4) and Chorea-tremor Controlled Zone were the top five acupoints that had been used. Superior-inferior acupoints matching was utilized the most. As to involved meridians, Du Meridian, Dan (Gallbladder) Meridian, Dachang (Large Intestine) Meridian, and Gan (Liver) Meridian were the most popular meridians. The application of meridians and acupoints for PD treatment lay emphasis on the acupoints on the head, attach importance to extinguishing Gan wind, tonifying qi and blood, and nourishing sinews, and make good use of superior-inferior acupoints matching.

75 FR 34666 - Stream Protection Rule; Environmental Impact Statement

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-18

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Chapter VII RIN 1029-AC63 Stream Protection Rule; Environmental Impact Statement AGENCY: Office of Surface Mining... impact statement. [[Page 34667

26 CFR 1.611-2 - Rules applicable to mines, oil and gas wells, and other natural deposits.

Code of Federal Regulations, 2014 CFR

2014-04-01

... Rules applicable to mines, oil and gas wells, and other natural deposits. (a) Computation of cost depletion of mines, oil and gas wells, and other natural deposits. (1) The basis upon which cost depletion... for the taxable year, the cost depletion for that year shall be computed by dividing such amount by...

DMET-Miner: Efficient discovery of association rules from pharmacogenomic data.

PubMed

Agapito, Giuseppe; Guzzi, Pietro H; Cannataro, Mario

2015-08-01

Microarray platforms enable the investigation of allelic variants that may be correlated to phenotypes. Among those, the Affymetrix DMET (Drug Metabolism Enzymes and Transporters) platform enables the simultaneous investigation of all the genes that are related to drug absorption, distribution, metabolism and excretion (ADME). Although recent studies demonstrated the effectiveness of the use of DMET data for studying drug response or toxicity in clinical studies, there is a lack of tools for the automatic analysis of DMET data. In a previous work we developed DMET-Analyzer, a methodology and a supporting platform able to automatize the statistical study of allelic variants, that has been validated in several clinical studies. Although DMET-Analyzer is able to correlate a single variant for each probe (related to a portion of a gene) through the use of the Fisher test, it is unable to discover multiple associations among allelic variants, due to its underlying statistic analysis strategy that focuses on a single variant for each time. To overcome those limitations, here we propose a new analysis methodology for DMET data based on Association Rules mining, and an efficient implementation of this methodology, named DMET-Miner. DMET-Miner extends the DMET-Analyzer tool with data mining capabilities and correlates the presence of a set of allelic variants with the conditions of patient's samples by exploiting association rules. To face the high number of frequent itemsets generated when considering large clinical studies based on DMET data, DMET-Miner uses an efficient data structure and implements an optimized search strategy that reduces the search space and the execution time. Preliminary experiments on synthetic DMET datasets, show how DMET-Miner outperforms off-the-shelf data mining suites such as the FP-Growth algorithms available in Weka and RapidMiner. To demonstrate the biological relevance of the extracted association rules and the effectiveness of the proposed approach from a medical point of view, some preliminary studies on a real clinical dataset are currently under medical investigation. Copyright © 2015 Elsevier Inc. All rights reserved.

76 FR 70075 - Proximity Detection Systems for Continuous Mining Machines in Underground Coal Mines

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-10

... Detection Systems for Continuous Mining Machines in Underground Coal Mines AGENCY: Mine Safety and Health... proposed rule addressing Proximity Detection Systems for Continuous Mining Machines in Underground Coal... Detection Systems for Continuous Mining Machines in Underground Coal Mines. MSHA conducted hearings on...

A rough set-based association rule approach implemented on a brand trust evaluation model

NASA Astrophysics Data System (ADS)

Liao, Shu-Hsien; Chen, Yin-Ju

2017-09-01

In commerce, businesses use branding to differentiate their product and service offerings from those of their competitors. The brand incorporates a set of product or service features that are associated with that particular brand name and identifies the product/service segmentation in the market. This study proposes a new data mining approach, a rough set-based association rule induction, implemented on a brand trust evaluation model. In addition, it presents as one way to deal with data uncertainty to analyse ratio scale data, while creating predictive if-then rules that generalise data values to the retail region. As such, this study uses the analysis of algorithms to find alcoholic beverages brand trust recall. Finally, discussions and conclusion are presented for further managerial implications.

Application of a hybrid association rules/decision tree model for drought monitoring

NASA Astrophysics Data System (ADS)

Nourani, Vahid; Molajou, Amir

2017-12-01

The previous researches have shown that the incorporation of the oceanic-atmospheric climate phenomena such as Sea Surface Temperature (SST) into hydro-climatic models could provide important predictive information about hydro-climatic variability. In this paper, the hybrid application of two data mining techniques (decision tree and association rules) was offered to discover affiliation between drought of Tabriz and Kermanshah synoptic stations (located in Iran) and de-trend SSTs of the Black, Mediterranean and Red Seas. Two major steps of the proposed model were the classification of de-trend SST data and selecting the most effective groups and extracting hidden information involved in the data. The techniques of decision tree which can identify the good traits from a data set for the classification purpose were used for classification and selecting the most effective groups and association rules were employed to extract the hidden predictive information from the large observed data. To examine the accuracy of the rules, confidence and Heidke Skill Score (HSS) measures were calculated and compared for different considering lag times. The computed measures confirm reliable performance of the proposed hybrid data mining method to forecast drought and the results show a relative correlation between the Mediterranean, Black and Red Sea de-trend SSTs and drought of Tabriz and Kermanshah synoptic stations so that the confidence between the monthly Standardized Precipitation Index (SPI) values and the de-trend SST of seas is higher than 70 and 80% respectively for Tabriz and Kermanshah synoptic stations.

Human Systems Integration (HSI) Associated Development Activities in Japan

DTIC Science & Technology

2008-06-12

machine learning and data mining methods. The continuous effort ( KAIZEN ) to improve the analysis phases are illustrated in Figure 14. Although there...model Extraction of a workflow Extraction of a control rule Variation analysis and improvement Plant operation KAIZEN Fig. 14

[Features of Professor Ma Kun's medication in treating ovulatory infertility].

PubMed

Tong, Ya-Jing; Zhang, Hui-Xian; Chen, Yan-Xia; Dong, Mei-Ling; Ma, Kun

2017-12-01

In order to analyze Professor Ma Kun's medication in treating anovulatory infertility, her prescriptions for treating anovulatory infertility in 2012-2015 were collected. The medication features and the regularity of prescriptions were mined by using traditional Chinese medicine inheritance support system, association rules, complex system entropy clustering and other mining methods. Finally, a total of 684 prescriptions and 300 kinds of herbs were screened out, with a total frequency of 11 156 times; And 68 core combinations and 8 new prescriptions were mined. The top three frequently used herbs by effect were respectively tonic herb, blood circulation promoting herb, and Qi-circulation promoting herb. The top three tastes were sweetness, bitterness and pungent flavor. The results showed 28 herbs with a high frequency of ≥100.The top 10 frequently used herbs were respectively Angelica Sinensis Radix, Cyperi Rhizoma, Chuanxiong Rhizome, Paeoniae Radix Rubra, Cyathulae Radix, Taxilli Herba, Cuscutae Semen, Codonopsis Radix, Ligustri Lucidi Fructus, Paeoniae Albaand Paeoniae Radix Alba. The association rules analysis showed commonly used herbal pairs, including Rehmanniae Radix Preparata-Chuanxiong Rhizome, Rehmanniae Radix Preparata-Angelica Sinensis Radix, Cuscutae Semen-Dipsaci Radix. In conclusion, Professor Ma has treated anovulatory infertility by nourishing the kidney and activating blood throughout the treatment course, and attached the importance to the relationship between Qi and blood and there gulation of liver, spleen and kidney in treating anovulatory infertility. Copyright© by the Chinese Pharmaceutical Association.

Research of Litchi Diseases Diagnosis Expertsystem Based on Rbr and Cbr

NASA Astrophysics Data System (ADS)

Xu, Bing; Liu, Liqun

To conquer the bottleneck problems existing in the traditional rule-based reasoning diseases diagnosis system, such as low reasoning efficiency and lack of flexibility, etc.. It researched the integrated case-based reasoning (CBR) and rule-based reasoning (RBR) technology, and put forward a litchi diseases diagnosis expert system (LDDES) with integrated reasoning method. The method use data mining and knowledge obtaining technology to establish knowledge base and case library. It adopt rules to instruct the retrieval and matching for CBR, and use association rule and decision trees algorithm to calculate case similarity.The experiment shows that the method can increase the system's flexibility and reasoning ability, and improve the accuracy of litchi diseases diagnosis.

77 FR 5740 - Tennessee Abandoned Mine Land Program

Federal Register 2010, 2011, 2012, 2013, 2014

2012-02-06

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 942... Mining Reclamation and Enforcement (OSM), Interior. ACTION: Proposed rule; public comment period and... amendment to the Tennessee Abandoned Mine Land (AML) Reclamation Plan under the Surface Mining Control and...

30 CFR 784.200 - Interpretive rules related to General Performance Standards.

Code of Federal Regulations, 2010 CFR

2010-07-01

... RECLAMATION AND OPERATION PLAN § 784.200 Interpretive rules related to General Performance Standards. The... ENFORCEMENT, DEPARTMENT OF THE INTERIOR SURFACE COAL MINING AND RECLAMATION OPERATIONS PERMITS AND COAL... Surface Mining Reclamation and Enforcement. (a) Interpretation of § 784.15: Reclamation plan: Postmining...

Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle

PubMed Central

Cerezo, Rebeca; Esteban, María; Sánchez-Santillán, Miguel; Núñez, José C.

2017-01-01

Introduction: Research about student performance has traditionally considered academic procrastination as a behavior that has negative effects on academic achievement. Although there is much evidence for this in class-based environments, there is a lack of research on Computer-Based Learning Environments (CBLEs). Therefore, the purpose of this study is to evaluate student behavior in a blended learning program and specifically procrastination behavior in relation to performance through Data Mining techniques. Materials and Methods: A sample of 140 undergraduate students participated in a blended learning experience implemented in a Moodle (Modular Object Oriented Developmental Learning Environment) Management System. Relevant interaction variables were selected for the study, taking into account student achievement and analyzing data by means of association rules, a mining technique. The association rules were arrived at and filtered through two selection criteria: 1, rules must have an accuracy over 0.8 and 2, they must be present in both sub-samples. Results: The findings of our study highlight the influence of time management in online learning environments, particularly on academic achievement, as there is an association between procrastination variables and student performance. Conclusion: Negative impact of procrastination in learning outcomes has been observed again but in virtual learning environments where practical implications, prevention of, and intervention in, are different from class-based learning. These aspects are discussed to help resolve student difficulties at various ages. PMID:28883801

Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle.

PubMed

Cerezo, Rebeca; Esteban, María; Sánchez-Santillán, Miguel; Núñez, José C

2017-01-01

Introduction: Research about student performance has traditionally considered academic procrastination as a behavior that has negative effects on academic achievement. Although there is much evidence for this in class-based environments, there is a lack of research on Computer-Based Learning Environments (CBLEs) . Therefore, the purpose of this study is to evaluate student behavior in a blended learning program and specifically procrastination behavior in relation to performance through Data Mining techniques. Materials and Methods: A sample of 140 undergraduate students participated in a blended learning experience implemented in a Moodle (Modular Object Oriented Developmental Learning Environment) Management System. Relevant interaction variables were selected for the study, taking into account student achievement and analyzing data by means of association rules, a mining technique. The association rules were arrived at and filtered through two selection criteria: 1, rules must have an accuracy over 0.8 and 2, they must be present in both sub-samples. Results: The findings of our study highlight the influence of time management in online learning environments, particularly on academic achievement, as there is an association between procrastination variables and student performance. Conclusion: Negative impact of procrastination in learning outcomes has been observed again but in virtual learning environments where practical implications, prevention of, and intervention in, are different from class-based learning. These aspects are discussed to help resolve student difficulties at various ages.

Traffic accident in Cuiabá-MT: an analysis through the data mining technology.

PubMed

Galvão, Noemi Dreyer; de Fátima Marin, Heimar

2010-01-01

The traffic road accidents (ATT) are non-intentional events with an important magnitude worldwide, mainly in the urban centers. This article aims to analyzes data related to the victims of ATT recorded by the Justice Secretariat and Public Security (SEJUSP) in hospital morbidity and mortality incidence at the city of Cuiabá-MT during 2006, using data mining technology. An observational, retrospective and exploratory study of the secondary data bases was carried out. The three database selected were related using the probabilistic method, through the free software RecLink. One hundred and thirty-nine (139) real pairs of victims of ATT were obtained. In this related database the data mining technology was applied with the software WEKA using the Apriori algorithm. The result generated 10 best rules, six of them were considered according to the parameters established that indicated a useful and comprehensible knowledge to characterize the victims of accidents in Cuiabá. Finally, the findings of the associative rules showed peculiarities of the road traffic accident victims in Cuiabá and highlight the need of prevention measures in the collision accidents for males.

A Study on Environmental Research Trends Using Text-Mining Method - Focus on Spatial information and ICT -

NASA Astrophysics Data System (ADS)

Lee, M. J.; Oh, K. Y.; Joung-ho, L.

2016-12-01

Recently there are many research about analysing the interaction between entities by text-mining analysis in various fields. In this paper, we aimed to quantitatively analyse research-trends in the area of environmental research relating either spatial information or ICT (Information and Communications Technology) by Text-mining analysis. To do this, we applied low-dimensional embedding method, clustering analysis, and association rule to find meaningful associative patterns of key words frequently appeared in the articles. As the authors suppose that KCI (Korea Citation Index) articles reflect academic demands, total 1228 KCI articles that have been published from 1996 to 2015 were reviewed and analysed by Text-mining method. First, we derived KCI articles from NDSL(National Discovery for Science Leaders) site. And then we pre-processed their key-words elected from abstract and then classified those in separable sectors. We investigated the appearance rates and association rule of key-words for articles in the two fields: spatial-information and ICT. In order to detect historic trends, analysis was conducted separately for the four periods: 1996-2000, 2001-2005, 2006-2010, 2011-2015. These analysis were conducted with the usage of R-software. As a result, we conformed that environmental research relating spatial information mainly focused upon such fields as `GIS(35%)', `Remote-Sensing(25%)', `environmental theme map(15.7%)'. Next, `ICT technology(23.6%)', `ICT service(5.4%)', `mobile(24%)', `big data(10%)', `AI(7%)' are primarily emerging from environmental research relating ICT. Thus, from the analysis results, this paper asserts that research trends and academic progresses are well-structured to review recent spatial information and ICT technology and the outcomes of the analysis can be an adequate guidelines to establish environment policies and strategies. KEY WORDS: Big data, Test-mining, Environmental research, Spatial-information, ICT Acknowledgements: The authors appreciate the support that this study has received from `Building application frame of environmental issues, to respond to the latest ICT trends'.

Personalised Information Services Using a Hybrid Recommendation Method Based on Usage Frequency

ERIC Educational Resources Information Center

Kim, Yong; Chung, Min Gyo

2008-01-01

Purpose: This paper seeks to describe a personal recommendation service (PRS) involving an innovative hybrid recommendation method suitable for deployment in a large-scale multimedia user environment. Design/methodology/approach: The proposed hybrid method partitions content and user into segments and executes association rule mining,…

Long-range prediction of Indian summer monsoon rainfall using data mining and statistical approaches

NASA Astrophysics Data System (ADS)

H, Vathsala; Koolagudi, Shashidhar G.

2017-10-01

This paper presents a hybrid model to better predict Indian summer monsoon rainfall. The algorithm considers suitable techniques for processing dense datasets. The proposed three-step algorithm comprises closed itemset generation-based association rule mining for feature selection, cluster membership for dimensionality reduction, and simple logistic function for prediction. The application of predicting rainfall into flood, excess, normal, deficit, and drought based on 36 predictors consisting of land and ocean variables is presented. Results show good accuracy in the considered study period of 37years (1969-2005).

Data Mining Methods for Recommender Systems

NASA Astrophysics Data System (ADS)

Amatriain, Xavier; Jaimes*, Alejandro; Oliver, Nuria; Pujol, Josep M.

In this chapter, we give an overview of the main Data Mining techniques used in the context of Recommender Systems. We first describe common preprocessing methods such as sampling or dimensionality reduction. Next, we review the most important classification techniques, including Bayesian Networks and Support Vector Machines. We describe the k-means clustering algorithm and discuss several alternatives. We also present association rules and related algorithms for an efficient training process. In addition to introducing these techniques, we survey their uses in Recommender Systems and present cases where they have been successfully applied.

77 FR 44155 - Administration of Mining Claims and Sites

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-27

... 1004-AE27 Administration of Mining Claims and Sites AGENCY: Bureau of Land Management, Interior. ACTION... on locating, recording, and maintaining mining claims or sites. In this rule, the BLM amends its... placer mining claims. The law specifies that the holder of an unpatented placer mining claim must pay the...

43 CFR 3487.1 - Logical mining units.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 43 Public Lands: Interior 2 2011-10-01 2011-10-01 false Logical mining units. 3487.1 Section 3487..., DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) COAL EXPLORATION AND MINING OPERATIONS RULES Logical Mining Unit § 3487.1 Logical mining units. (a) An LMU shall become effective only upon approval of the...

[Analysis of on medication rules for Qi-deficiency and blood-stasis syndrome of chronic heart failure based on data mining technology].

PubMed

Wang, Qian; Yao, Geng-Zhen; Pan, Guang-Ming; Huang, Jing-Yi; An, Yi-Pei; Zou, Xu

2017-01-01

To analyze the medication features and the regularity of prescriptions of traditional Chinese medicine in treating patients with Qi-deficiency and blood-stasis syndrome of chronic heart failure based on modern literature. In this article, CNKI Chinese academic journal database, Wanfang Chinese academic journal database and VIP Chinese periodical database were all searched from January 2000 to December 2015 for the relevant literature on traditional Chinese medicine treatment for Qi-deficiency and blood-stasis syndrome of chronic heart failure. Then a normalized database was established for further data mining and analysis. Subsequently, the medication features and the regularity of prescriptions were mined by using traditional Chinese medicine inheritance support system(V2.5), association rules, improved mutual information algorithm, complex system entropy clustering and other mining methods. Finally, a total of 171 articles were included, involving 171 prescriptions, 140 kinds of herbs, with a total frequency of 1 772 for the herbs. As a result, 19 core prescriptions and 7 new prescriptions were mined. The most frequently used herbs included Huangqi(Astragali Radix), Danshen(Salviae Miltiorrhizae Radix et Rhizoma), Fuling(Poria), Renshen(Ginseng Radix et Rhizoma), Tinglizi(Semen Lepidii), Baizhu(Atractylodis Macrocephalae Rhizoma), and Guizhi(Cinnamomum Ramulus). The core prescriptions were composed of Huangqi(Astragali Radix), Danshen(Salviae Miltiorrhizae Radix et Rhizoma) and Fuling(Poria), etc. The high frequent herbs and core prescriptions not only highlight the medication features of Qi-invigorating and blood-circulating therapy, but also reflect the regularity of prescriptions of blood-circulating, Yang-warming, and urination-promoting therapy based on syndrome differentiation. Moreover, the mining of the new prescriptions provide new reference and inspiration for clinical treatment of various accompanying symptoms of chronic heart failure. In conclusion, this article provides new reference for traditional Chinese medicine in the treatment of chronic heart failure. Copyright© by the Chinese Pharmaceutical Association.

Health-Mining: a Disease Management Support Service based on Data Mining and Rule Extraction.

PubMed

Bei, Andrea; De Luca, Stefano; Ruscitti, Giancarlo; Salamon, Diego

2005-01-01

The disease management is the collection of the processes aimed to control the health care and improving the quality at same time reducing the overall cost of the procedures. Our system, Health-Mining, is a Decision Support System with the objective of controlling the adequacy of hospitalization and therapies, determining the effective use of standard guidelines and eventually identifying better ones emerged from the medical practice (Evidence Based Medicine). In realizing the system, we have the aim of creation of a path to admissions- appropriateness criteria construction, valid at an international level. A main goal of the project is rule extraction and the identification of the rules adequate in term of efficacy, quality and cost reduction, especially in the view of fast changing technologies and medicines. We tested Health-Mining in a real test case for an Italian Region, Regione Veneto, on the installation of pacemaker and ICD.

A primer to frequent itemset mining for bioinformatics

PubMed Central

Naulaerts, Stefan; Meysman, Pieter; Bittremieux, Wout; Vu, Trung Nghia; Vanden Berghe, Wim; Goethals, Bart

2015-01-01

Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur. An archetypical example is the identification of products that often end up together in the same shopping basket in supermarket transactions. A number of algorithms have been developed to address variations of this computationally non-trivial problem. Frequent itemset mining techniques are able to efficiently capture the characteristics of (complex) data and succinctly summarize it. Owing to these and other interesting properties, these techniques have proven their value in biological data analysis. Nevertheless, information about the bioinformatics applications of these techniques remains scattered. In this primer, we introduce frequent itemset mining and their derived association rules for life scientists. We give an overview of various algorithms, and illustrate how they can be used in several real-life bioinformatics application domains. We end with a discussion of the future potential and open challenges for frequent itemset mining in the life sciences. PMID:24162173

76 FR 35801 - Examinations of Work Areas in Underground Coal Mines and Pattern of Violations

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-20

..., 1219-AB73 Examinations of Work Areas in Underground Coal Mines and Pattern of Violations AGENCY: Mine... public hearings on the Agency's proposed rules for Examinations of Work Areas in Underground Coal Mines... Underground Coal Mines' submissions, and with ``RIN 1219-AB73'' for Pattern of Violations' submissions...

78 FR 48591 - Refuge Alternatives for Underground Coal Mines

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-08

... Administration 30 CFR Parts 7 and 75 Refuge Alternatives for Underground Coal Mines; Proposed Rules #0;#0;Federal... Underground Coal Mines AGENCY: Mine Safety and Health Administration, Labor. ACTION: Limited reopening of the... for miners to deploy and use refuge alternatives in underground coal mines. The U.S. Court of Appeals...

75 FR 20918 - High-Voltage Continuous Mining Machine Standard for Underground Coal Mines

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-22

... DEPARTMENT OF LABOR Mine Safety and Health Administration 30 CFR Parts 18 and 75 RIN 1219-AB34 High-Voltage Continuous Mining Machine Standard for Underground Coal Mines Correction In rule document 2010-7309 beginning on page 17529 in the issue of Tuesday, April 6, 2010, make the following correction...

43 CFR 3482.3 - Mining operations maps.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 43 Public Lands: Interior 2 2011-10-01 2011-10-01 false Mining operations maps. 3482.3 Section... MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) COAL EXPLORATION AND MINING OPERATIONS RULES Exploration and Resource Recovery and Protection Plans § 3482.3 Mining operations maps. (a...

Rules of co-occurring mutations characterize the antigenic evolution of human influenza A/H3N2, A/H1N1 and B viruses.

PubMed

Chen, Haifen; Zhou, Xinrui; Zheng, Jie; Kwoh, Chee-Keong

2016-12-05

The human influenza viruses undergo rapid evolution (especially in hemagglutinin (HA), a glycoprotein on the surface of the virus), which enables the virus population to constantly evade the human immune system. Therefore, the vaccine has to be updated every year to stay effective. There is a need to characterize the evolution of influenza viruses for better selection of vaccine candidates and the prediction of pandemic strains. Studies have shown that the influenza hemagglutinin evolution is driven by the simultaneous mutations at antigenic sites. Here, we analyze simultaneous or co-occurring mutations in the HA protein of human influenza A/H3N2, A/H1N1 and B viruses to predict potential mutations, characterizing the antigenic evolution. We obtain the rules of mutation co-occurrence using association rule mining after extracting HA1 sequences and detect co-mutation sites under strong selective pressure. Then we predict the potential drifts with specific mutations of the viruses based on the rules and compare the results with the "observed" mutations in different years. The sites under frequent mutations are in antigenic regions (epitopes) or receptor binding sites. Our study demonstrates the co-occurring site mutations obtained by rule mining can capture the evolution of influenza viruses, and confirms that cooperative interactions among sites of HA1 protein drive the influenza antigenic evolution.

Discovering Prerequisite Structure of Skills through Probabilistic Association Rules Mining

ERIC Educational Resources Information Center

Chen, Yang; Wuillemin, Pierre-Henr; Labat, Jean-Marc

2015-01-01

Estimating the prerequisite structure of skills is a crucial issue in domain modeling. Students usually learn skills in sequence since the preliminary skills need to be learned prior to the complex skills. The prerequisite relations between skills underlie the design of learning sequence and adaptation strategies for tutoring systems. The…

Cheminformatics and Data Mining Approaches for Exploring the Alternatives Testing Landscsape: Case Studies in ToxCast and Skin Sensitization (BOSC CSS)

EPA Science Inventory

Cheminformatics approaches and structure-based rules are being used to evaluate and explore the ToxCast chemical landscape and associated high-throughput screening (HTS) data. We have shown that the library provides comprehensive coverage of the knowledge domains and target inven...

Association mining of mutated cancer genes in different clinical stages across 11 cancer types.

PubMed

Hu, Wangxiong; Li, Xiaofen; Wang, Tingzhang; Zheng, Shu

2016-10-18

Many studies have demonstrated that some genes (e.g. APC, BRAF, KRAS, PTEN, TP53) are frequently mutated in cancer, however, underlying mechanism that contributes to their high mutation frequency remains unclear. Here we used Apriori algorithm to find the frequent mutational gene sets (FMGSs) from 4,904 tumors across 11 cancer types as part of the TCGA Pan-Cancer effort and then mined the hidden association rules (ARs) within these FMGSs. Intriguingly, we found that well-known cancer driver genes such as BRAF, KRAS, PTEN, and TP53 were often co-occurred with other driver genes and FMGSs size peaked at an itemset size of 3~4 genes. Besides, the number and constitution of FMGS and ARs differed greatly among different cancers and stages. In addition, FMGS and ARs were rare in endocrine-related cancers such as breast carcinoma, ovarian cystadenocarcinoma, and thyroid carcinoma, but abundant in cancers contact directly with external environments such as skin melanoma and stomach adenocarcinoma. Furthermore, we observed more rules in stage IV than in other stages, indicating that distant metastasis needed more sophisticated gene regulatory network.

76 FR 12852 - Louisiana Regulatory Program/Abandoned Mine Land Reclamation Plan

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-09

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 918... Reclamation Plan AGENCY: Office of Surface Mining Reclamation and Enforcement, Interior. ACTION: Final rule; approval of amendment. SUMMARY: We, the Office of Surface Mining Reclamation and Enforcement (OSM), are...

75 FR 60373 - Louisiana Regulatory Program/Abandoned Mine Land Reclamation Plan

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-30

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 918... Reclamation Plan AGENCY: Office of Surface Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule... of Surface Mining Reclamation and Enforcement (OSM), are announcing receipt of a proposed amendment...

26 CFR 1.614-3 - Rules relating to separate operating mineral interests in the case of mines.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 26 Internal Revenue 7 2011-04-01 2009-04-01 true Rules relating to separate operating mineral interests in the case of mines. 1.614-3 Section 1.614-3 Internal Revenue INTERNAL REVENUE SERVICE, DEPARTMENT OF THE TREASURY (CONTINUED) INCOME TAX (CONTINUED) INCOME TAXES (CONTINUED) Natural Resources § 1...

26 CFR 1.611-2 - Rules applicable to mines, oil and gas wells, and other natural deposits.

Code of Federal Regulations, 2011 CFR

2011-04-01

... other natural deposits. 1.611-2 Section 1.611-2 Internal Revenue INTERNAL REVENUE SERVICE, DEPARTMENT OF THE TREASURY (CONTINUED) INCOME TAX (CONTINUED) INCOME TAXES (CONTINUED) Natural Resources § 1.611-2 Rules applicable to mines, oil and gas wells, and other natural deposits. (a) Computation of cost...

30 CFR 49.60 - Requirements for a local mine rescue contest.

Code of Federal Regulations, 2010 CFR

2010-07-01

... EDUCATION AND TRAINING MINE RESCUE TEAMS Mine Rescue Teams for Underground Coal Mines § 49.60 Requirements... United States; (2) Uses MSHA-recognized rules; (3) Has a minimum of three mine rescue teams competing; (4) Has one or more problems conducted on one or more days with a determined winner; (5) Includes team...

30 CFR 49.60 - Requirements for a local mine rescue contest.

Code of Federal Regulations, 2013 CFR

2013-07-01

... EDUCATION AND TRAINING MINE RESCUE TEAMS Mine Rescue Teams for Underground Coal Mines § 49.60 Requirements... United States; (2) Uses MSHA-recognized rules; (3) Has a minimum of three mine rescue teams competing; (4) Has one or more problems conducted on one or more days with a determined winner; (5) Includes team...

30 CFR 49.60 - Requirements for a local mine rescue contest.

Code of Federal Regulations, 2012 CFR

2012-07-01

... EDUCATION AND TRAINING MINE RESCUE TEAMS Mine Rescue Teams for Underground Coal Mines § 49.60 Requirements... United States; (2) Uses MSHA-recognized rules; (3) Has a minimum of three mine rescue teams competing; (4) Has one or more problems conducted on one or more days with a determined winner; (5) Includes team...

30 CFR 49.60 - Requirements for a local mine rescue contest.

Code of Federal Regulations, 2011 CFR

2011-07-01

... EDUCATION AND TRAINING MINE RESCUE TEAMS Mine Rescue Teams for Underground Coal Mines § 49.60 Requirements... United States; (2) Uses MSHA-recognized rules; (3) Has a minimum of three mine rescue teams competing; (4) Has one or more problems conducted on one or more days with a determined winner; (5) Includes team...

30 CFR 49.60 - Requirements for a local mine rescue contest.

Code of Federal Regulations, 2014 CFR

2014-07-01

... EDUCATION AND TRAINING MINE RESCUE TEAMS Mine Rescue Teams for Underground Coal Mines § 49.60 Requirements... United States; (2) Uses MSHA-recognized rules; (3) Has a minimum of three mine rescue teams competing; (4) Has one or more problems conducted on one or more days with a determined winner; (5) Includes team...

Order Batching in Warehouses by Minimizing Total Tardiness: A Hybrid Approach of Weighted Association Rule Mining and Genetic Algorithms

PubMed Central

Taheri, Shahrooz; Mat Saman, Muhamad Zameri; Wong, Kuan Yew

2013-01-01

One of the cost-intensive issues in managing warehouses is the order picking problem which deals with the retrieval of items from their storage locations in order to meet customer requests. Many solution approaches have been proposed in order to minimize traveling distance in the process of order picking. However, in practice, customer orders have to be completed by certain due dates in order to avoid tardiness which is neglected in most of the related scientific papers. Consequently, we proposed a novel solution approach in order to minimize tardiness which consists of four phases. First of all, weighted association rule mining has been used to calculate associations between orders with respect to their due date. Next, a batching model based on binary integer programming has been formulated to maximize the associations between orders within each batch. Subsequently, the order picking phase will come up which used a Genetic Algorithm integrated with the Traveling Salesman Problem in order to identify the most suitable travel path. Finally, the Genetic Algorithm has been applied for sequencing the constructed batches in order to minimize tardiness. Illustrative examples and comparisons are presented to demonstrate the proficiency and solution quality of the proposed approach. PMID:23864823

Order batching in warehouses by minimizing total tardiness: a hybrid approach of weighted association rule mining and genetic algorithms.

PubMed

Azadnia, Amir Hossein; Taheri, Shahrooz; Ghadimi, Pezhman; Saman, Muhamad Zameri Mat; Wong, Kuan Yew

2013-01-01

One of the cost-intensive issues in managing warehouses is the order picking problem which deals with the retrieval of items from their storage locations in order to meet customer requests. Many solution approaches have been proposed in order to minimize traveling distance in the process of order picking. However, in practice, customer orders have to be completed by certain due dates in order to avoid tardiness which is neglected in most of the related scientific papers. Consequently, we proposed a novel solution approach in order to minimize tardiness which consists of four phases. First of all, weighted association rule mining has been used to calculate associations between orders with respect to their due date. Next, a batching model based on binary integer programming has been formulated to maximize the associations between orders within each batch. Subsequently, the order picking phase will come up which used a Genetic Algorithm integrated with the Traveling Salesman Problem in order to identify the most suitable travel path. Finally, the Genetic Algorithm has been applied for sequencing the constructed batches in order to minimize tardiness. Illustrative examples and comparisons are presented to demonstrate the proficiency and solution quality of the proposed approach.

76 FR 76104 - Arkansas Regulatory Program and Abandoned Mine Land Reclamation Plan

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-06

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 904... Reclamation Plan AGENCY: Office of Surface Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period on proposed amendment. SUMMARY: We, the Office of Surface Mining Reclamation and...

77 FR 55430 - Arkansas Regulatory Program and Abandoned Mine Land Reclamation Plan

Federal Register 2010, 2011, 2012, 2013, 2014

2012-09-10

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 904... Reclamation Plan AGENCY: Office of Surface Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period on proposed amendment. SUMMARY: We, the Office of Surface Mining Reclamation and...

Dietary patterns analysis using data mining method. An application to data from the CYKIDS study.

PubMed

Lazarou, Chrystalleni; Karaolis, Minas; Matalas, Antonia-Leda; Panagiotakos, Demosthenes B

2012-11-01

Data mining is a computational method that permits the extraction of patterns from large databases. We applied the data mining approach in data from 1140 children (9-13 years), in order to derive dietary habits related to children's obesity status. Rules emerged via data mining approach revealed the detrimental influence of the increased consumption of soft dinks, delicatessen meat, sweets, fried and junk food. For example, frequent (3-5 times/week) consumption of all these foods increases the risk for being obese by 75%, whereas in children who have a similar dietary pattern, but eat >2 times/week fish and seafood the risk for obesity is reduced by 33%. In conclusion patterns revealed from data mining technique refer to specific groups of children and demonstrate the effect on the risk associated with obesity status when a single dietary habit might be modified. Thus, a more individualized approach when translating public health messages could be achieved. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Prediction model for peninsular Indian summer monsoon rainfall using data mining and statistical approaches

NASA Astrophysics Data System (ADS)

Vathsala, H.; Koolagudi, Shashidhar G.

2017-01-01

In this paper we discuss a data mining application for predicting peninsular Indian summer monsoon rainfall, and propose an algorithm that combine data mining and statistical techniques. We select likely predictors based on association rules that have the highest confidence levels. We then cluster the selected predictors to reduce their dimensions and use cluster membership values for classification. We derive the predictors from local conditions in southern India, including mean sea level pressure, wind speed, and maximum and minimum temperatures. The global condition variables include southern oscillation and Indian Ocean dipole conditions. The algorithm predicts rainfall in five categories: Flood, Excess, Normal, Deficit and Drought. We use closed itemset mining, cluster membership calculations and a multilayer perceptron function in the algorithm to predict monsoon rainfall in peninsular India. Using Indian Institute of Tropical Meteorology data, we found the prediction accuracy of our proposed approach to be exceptionally good.

E-book recommender system design and implementation based on data mining

NASA Astrophysics Data System (ADS)

Wang, Zongjiang

2011-12-01

In the knowledge explosion, rapid development of information age, how quickly the user or users interested in useful information for feedback to the user problem to be solved in this article. This paper based on data mining, association rules to the model and classification model a combination of electronic books on the recommendation of the user's neighboring users interested in e-books to target users. Introduced the e-book recommendation and the key technologies, system implementation algorithms, and implementation process, was proved through experiments that this system can help users quickly find the required e-books.

76 FR 64047 - Montana Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-17

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 926... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and... amendment to the Montana regulatory program (hereinafter, the ``Montana program'') under the Surface Mining...

76 FR 36040 - Wyoming Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-21

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 950... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and... amendment to the Wyoming regulatory program (hereinafter, the ``Wyoming program'') under the Surface Mining...

78 FR 16204 - Wyoming Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-14

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 950... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and... amendment to the Wyoming regulatory program (hereinafter, the ``Wyoming program'') under the Surface Mining...

76 FR 80310 - Wyoming Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-23

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 950... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and... amendment to the Wyoming regulatory program (hereinafter, the ``Wyoming program'') under the Surface Mining...

76 FR 67635 - Alaska Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-02

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 902... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and... amendment to the Alaska regulatory program (hereinafter, the ``Alaska program'') under the Surface Mining...

76 FR 64045 - Montana Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-17

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 926... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and... amendment to the Montana regulatory program (hereinafter, the ``Montana program'') under the Surface Mining...

76 FR 76111 - Montana Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-06

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 926... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and... amendment to the Montana regulatory program (hereinafter, the ``Montana program'') under the Surface Mining...

77 FR 25874 - Pennsylvania Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-02

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 938... Mining Reclamation and Enforcement (OSM), Interior. ACTION: Final rule; removal of required amendment... regulatory program (the ``Pennsylvania program'') regulations under the Surface Mining Control and...

TSCA Chemical Data Reporting Fact Sheet: Reporting Manufactured Chemical Substances from Metal Mining and Related Activities

EPA Pesticide Factsheets

This fact sheet provides guidance on the Chemical Data Reporting (CDR) rule requirements related to the reporting of mined metals, intermediates, and byproducts manufactured during metal mining and related activities.

77 FR 1430 - Maryland Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2012-01-10

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 920... Mining Reclamation and Enforcement (OSM), Interior. ACTION: Proposed rule; extension of the comment... the Maryland regulatory program (the ``Maryland program'') under the Surface Mining Control and...

Data mining for multiagent rules, strategies, and fuzzy decision tree structure

NASA Astrophysics Data System (ADS)

Smith, James F., III; Rhyne, Robert D., II; Fisher, Kristin

2002-03-01

A fuzzy logic based resource manager (RM) has been developed that automatically allocates electronic attack resources in real-time over many dissimilar platforms. Two different data mining algorithms have been developed to determine rules, strategies, and fuzzy decision tree structure. The first data mining algorithm uses a genetic algorithm as a data mining function and is called from an electronic game. The game allows a human expert to play against the resource manager in a simulated battlespace with each of the defending platforms being exclusively directed by the fuzzy resource manager and the attacking platforms being controlled by the human expert or operating autonomously under their own logic. This approach automates the data mining problem. The game automatically creates a database reflecting the domain expert's knowledge. It calls a data mining function, a genetic algorithm, for data mining of the database as required and allows easy evaluation of the information mined in the second step. The criterion for re- optimization is discussed as well as experimental results. Then a second data mining algorithm that uses a genetic program as a data mining function is introduced to automatically discover fuzzy decision tree structures. Finally, a fuzzy decision tree generated through this process is discussed.

Research on PM2.5 time series characteristics based on data mining technology

NASA Astrophysics Data System (ADS)

Zhao, Lifang; Jia, Jin

2018-02-01

With the development of data mining technology and the establishment of environmental air quality database, it is necessary to discover the potential correlations and rules by digging the massive environmental air quality information and analyzing the air pollution process. In this paper, we have presented a sequential pattern mining method based on the air quality data and pattern association technology to analyze the PM2.5 time series characteristics. Utilizing the real-time monitoring data of urban air quality in China, the time series rule and variation properties of PM2.5 under different pollution levels are extracted and analyzed. The analysis results show that the time sequence features of the PM2.5 concentration is directly affected by the alteration of the pollution degree. The longest time that PM2.5 remained stable is about 24 hours. As the pollution degree gets severer, the instability time and step ascending time gradually changes from 12-24 hours to 3 hours. The presented method is helpful for the controlling and forecasting of the air quality while saving the measuring costs, which is of great significance for the government regulation and public prevention of the air pollution.

Data mining for the identification of metabolic syndrome status

PubMed Central

Worachartcheewan, Apilak; Schaduangrat, Nalini; Prachayasittikul, Virapong; Nantasenamat, Chanin

2018-01-01

Metabolic syndrome (MS) is a condition associated with metabolic abnormalities that are characterized by central obesity (e.g. waist circumference or body mass index), hypertension (e.g. systolic or diastolic blood pressure), hyperglycemia (e.g. fasting plasma glucose) and dyslipidemia (e.g. triglyceride and high-density lipoprotein cholesterol). It is also associated with the development of diabetes mellitus (DM) type 2 and cardiovascular disease (CVD). Therefore, the rapid identification of MS is required to prevent the occurrence of such diseases. Herein, we review the utilization of data mining approaches for MS identification. Furthermore, the concept of quantitative population-health relationship (QPHR) is also presented, which can be defined as the elucidation/understanding of the relationship that exists between health parameters and health status. The QPHR modeling uses data mining techniques such as artificial neural network (ANN), support vector machine (SVM), principal component analysis (PCA), decision tree (DT), random forest (RF) and association analysis (AA) for modeling and construction of predictive models for MS characterization. The DT method has been found to outperform other data mining techniques in the identification of MS status. Moreover, the AA technique has proved useful in the discovery of in-depth as well as frequently occurring health parameters that can be used for revealing the rules of MS development. This review presents the potential benefits on the applications of data mining as a rapid identification tool for classifying MS. PMID:29383020

Data mining for the identification of metabolic syndrome status.

PubMed

Worachartcheewan, Apilak; Schaduangrat, Nalini; Prachayasittikul, Virapong; Nantasenamat, Chanin

2018-01-01

Metabolic syndrome (MS) is a condition associated with metabolic abnormalities that are characterized by central obesity (e.g. waist circumference or body mass index), hypertension (e.g. systolic or diastolic blood pressure), hyperglycemia (e.g. fasting plasma glucose) and dyslipidemia (e.g. triglyceride and high-density lipoprotein cholesterol). It is also associated with the development of diabetes mellitus (DM) type 2 and cardiovascular disease (CVD). Therefore, the rapid identification of MS is required to prevent the occurrence of such diseases. Herein, we review the utilization of data mining approaches for MS identification. Furthermore, the concept of quantitative population-health relationship (QPHR) is also presented, which can be defined as the elucidation/understanding of the relationship that exists between health parameters and health status. The QPHR modeling uses data mining techniques such as artificial neural network (ANN), support vector machine (SVM), principal component analysis (PCA), decision tree (DT), random forest (RF) and association analysis (AA) for modeling and construction of predictive models for MS characterization. The DT method has been found to outperform other data mining techniques in the identification of MS status. Moreover, the AA technique has proved useful in the discovery of in-depth as well as frequently occurring health parameters that can be used for revealing the rules of MS development. This review presents the potential benefits on the applications of data mining as a rapid identification tool for classifying MS.

75 FR 69617 - Lowering Miners' Exposure to Respirable Coal Mine Dust, Including Continuous Personal Dust Monitors

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-15

... 1219-AB64 Lowering Miners' Exposure to Respirable Coal Mine Dust, Including Continuous Personal Dust... hearings on the proposed rule addressing Lowering Miners' Exposure to Respirable Coal Mine Dust, Including... miners' exposure to respirable coal mine dust by revising the Agency's existing standards on miners...

76 FR 11187 - Examinations of Work Areas in Underground Coal Mines for Violations of Mandatory Health or Safety...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-01

... Examinations of Work Areas in Underground Coal Mines for Violations of Mandatory Health or Safety Standards... rule addressing Examinations of Work Areas in Underground Coal Mines for Violations of Mandatory Health..., and weekly examinations of underground coal mines. This extension gives commenters an additional 30...

77 FR 58056 - Mississippi Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2012-09-19

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 924... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and opportunity for public hearing. SUMMARY: We, the Office of Surface Mining Reclamation and Enforcement (OSM...

76 FR 36039 - Colorado Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-21

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 906... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and... Mining Control and Reclamation Act of 1977 (``SMCRA'' or ``the Act''). Colorado proposes both additions...

77 FR 34890 - Oklahoma Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-12

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 936... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and opportunity for public hearing on proposed amendment. SUMMARY: We, the Office of Surface Mining Reclamation...

76 FR 50708 - Texas Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-16

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 943... AGENCY: Office of Surface Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and opportunity for public hearing. SUMMARY: We, the Office of Surface Mining Reclamation...

75 FR 60371 - Alabama Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-30

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 901... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and opportunity for public hearing on proposed amendment. SUMMARY: We, the Office of Surface Mining Reclamation...

77 FR 41680 - Indiana Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-16

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 914... Mining Reclamation and Enforcement, Interior. ACTION: Final rule; approval of amendment. SUMMARY: We, the Office of Surface Mining Reclamation and Enforcement (OSM), are approving amendments to the Indiana...

77 FR 25949 - Texas Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-02

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 943... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and opportunity for public hearing on proposed amendment. SUMMARY: We, the Office of Surface Mining Reclamation...

76 FR 76109 - Colorado Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-06

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 906... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; reopening and extension of public...'') under the Surface Mining Control and Reclamation Act of 1977 (``SMCRA'' or ``the Act''). Colorado...

77 FR 66574 - Texas Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2012-11-06

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 943... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and opportunity for public hearing on proposed amendment. SUMMARY: We, the Office of Surface Mining Reclamation...

77 FR 18149 - Montana Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2012-03-27

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 926... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; reopening and extension of public... receipt of Montana's response to the Office of Surface Mining Reclamation and Enforcement's (OSM) November...

77 FR 24661 - North Dakota Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2012-04-25

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 934... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and... Surface Mining Control and Reclamation Act of 1977 (``SMCRA'' or ``the Act''). North Dakota proposes...

76 FR 23522 - Oklahoma Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-04-27

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 936... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and opportunity for public hearing. SUMMARY: We, the Office of Surface Mining Reclamation and Enforcement (OSM...

75 FR 21534 - Texas Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-26

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 943... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and opportunity for public hearing on proposed amendment. SUMMARY: We, the Office of Surface Mining Reclamation...

77 FR 34892 - Utah Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-12

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 944... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and opportunity for public hearing on proposed amendment. SUMMARY: We, the Office of Surface Mining Reclamation...

77 FR 18738 - Texas Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2012-03-28

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 943... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and opportunity for public hearing on proposed amendment. SUMMARY: We, the Office of Surface Mining Reclamation...

76 FR 9700 - Alabama Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-02-22

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 901... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and opportunity for public hearing on proposed amendment. SUMMARY: We, the Office of Surface Mining Reclamation...

77 FR 40796 - Wyoming Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-11

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 950... Mining Reclamation and Enforcement, Interior. ACTION: Final rule. SUMMARY: We, the Office of Surface Mining Reclamation and Enforcement (OSM), are removing a disapproval codified in OSM regulations...

76 FR 12857 - Montana Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-09

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 926... of Surface Mining Reclamation and Enforcement, Interior. ACTION: Final rule; approval of amendment... the Surface Mining Control and Reclamation Act of 1977 (``SMCRA'' or ``the Act''). Montana proposed...

78 FR 11617 - Pennsylvania Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-19

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 938... Surface Mining Reclamation and Enforcement (OSM), Interior. ACTION: Proposed rule; reopening of comment... regulatory program (the ``Pennsylvania program'') under the Surface Mining Control and Reclamation Act of...

Pattern Mining for Extraction of mentions of Adverse Drug Reactions from User Comments

PubMed Central

Nikfarjam, Azadeh; Gonzalez, Graciela H.

2011-01-01

Rapid growth of online health social networks has enabled patients to communicate more easily with each other. This way of exchange of opinions and experiences has provided a rich source of information about drugs and their effectiveness and more importantly, their possible adverse reactions. We developed a system to automatically extract mentions of Adverse Drug Reactions (ADRs) from user reviews about drugs in social network websites by mining a set of language patterns. The system applied association rule mining on a set of annotated comments to extract the underlying patterns of colloquial expressions about adverse effects. The patterns were tested on a set of unseen comments to evaluate their performance. We reached to precision of 70.01% and recall of 66.32% and F-measure of 67.96%. PMID:22195162

Granular support vector machines with association rules mining for protein homology prediction.

PubMed

Tang, Yuchun; Jin, Bo; Zhang, Yan-Qing

2005-01-01

Protein homology prediction between protein sequences is one of critical problems in computational biology. Such a complex classification problem is common in medical or biological information processing applications. How to build a model with superior generalization capability from training samples is an essential issue for mining knowledge to accurately predict/classify unseen new samples and to effectively support human experts to make correct decisions. A new learning model called granular support vector machines (GSVM) is proposed based on our previous work. GSVM systematically and formally combines the principles from statistical learning theory and granular computing theory and thus provides an interesting new mechanism to address complex classification problems. It works by building a sequence of information granules and then building support vector machines (SVM) in some of these information granules on demand. A good granulation method to find suitable granules is crucial for modeling a GSVM with good performance. In this paper, we also propose an association rules-based granulation method. For the granules induced by association rules with high enough confidence and significant support, we leave them as they are because of their high "purity" and significant effect on simplifying the classification task. For every other granule, a SVM is modeled to discriminate the corresponding data. In this way, a complex classification problem is divided into multiple smaller problems so that the learning task is simplified. The proposed algorithm, here named GSVM-AR, is compared with SVM by KDDCUP04 protein homology prediction data. The experimental results show that finding the splitting hyperplane is not a trivial task (we should be careful to select the association rules to avoid overfitting) and GSVM-AR does show significant improvement compared to building one single SVM in the whole feature space. Another advantage is that the utility of GSVM-AR is very good because it is easy to be implemented. More importantly and more interestingly, GSVM provides a new mechanism to address complex classification problems.

Use HypE to Hide Association Rules by Adding Items

PubMed Central

Cheng, Peng; Lin, Chun-Wei; Pan, Jeng-Shyang

2015-01-01

During business collaboration, partners may benefit through sharing data. People may use data mining tools to discover useful relationships from shared data. However, some relationships are sensitive to the data owners and they hope to conceal them before sharing. In this paper, we address this problem in forms of association rule hiding. A hiding method based on evolutionary multi-objective optimization (EMO) is proposed, which performs the hiding task by selectively inserting items into the database to decrease the confidence of sensitive rules below specified thresholds. The side effects generated during the hiding process are taken as optimization goals to be minimized. HypE, a recently proposed EMO algorithm, is utilized to identify promising transactions for modification to minimize side effects. Results on real datasets demonstrate that the proposed method can effectively perform sanitization with fewer damages to the non-sensitive knowledge in most cases. PMID:26070130

Multiagent data warehousing and multiagent data mining for cerebrum/cerebellum modeling

NASA Astrophysics Data System (ADS)

Zhang, Wen-Ran

2002-03-01

An algorithm named Neighbor-Miner is outlined for multiagent data warehousing and multiagent data mining. The algorithm is defined in an evolving dynamic environment with autonomous or semiautonomous agents. Instead of mining frequent itemsets from customer transactions, the new algorithm discovers new agents and mining agent associations in first-order logic from agent attributes and actions. While the Apriori algorithm uses frequency as a priory threshold, the new algorithm uses agent similarity as priory knowledge. The concept of agent similarity leads to the notions of agent cuboid, orthogonal multiagent data warehousing (MADWH), and multiagent data mining (MADM). Based on agent similarities and action similarities, Neighbor-Miner is proposed and illustrated in a MADWH/MADM approach to cerebrum/cerebellum modeling. It is shown that (1) semiautonomous neurofuzzy agents can be identified for uniped locomotion and gymnastic training based on attribute relevance analysis; (2) new agents can be discovered and agent cuboids can be dynamically constructed in an orthogonal MADWH, which resembles an evolving cerebrum/cerebellum system; and (3) dynamic motion laws can be discovered as association rules in first order logic. Although examples in legged robot gymnastics are used to illustrate the basic ideas, the new approach is generally suitable for a broad category of data mining tasks where knowledge can be discovered collectively by a set of agents from a geographically or geometrically distributed but relevant environment, especially in scientific and engineering data environments.

78 FR 6062 - North Dakota Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-29

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 934... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and... Surface Mining Control and Reclamation Act of 1977 (``SMCRA'' or ``the Act''). North Dakota intends to...

76 FR 4266 - New Mexico Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-01-25

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 931... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and... Mining Control and Reclamation Act of 1977 (``SMCRA'' or ``the Act''). New Mexico proposes revisions to...

76 FR 9642 - Alabama Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-02-22

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 901... Mining Reclamation and Enforcement, Interior. ACTION: Final rule; approval of amendment. SUMMARY: We, the Office of Surface Mining Reclamation and Enforcement (OSM), are approving an amendment to the Alabama...

78 FR 13002 - Pennsylvania Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-26

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 938... Mining Reclamation and Enforcement (``OSM''), Interior. ACTION: Proposed rule; public comment period and... regulatory program under the Surface Mining Control and Reclamation Act of 1977 (``SMCRA'' or the ``Act...

78 FR 11579 - Texas Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-19

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 943... Mining Reclamation and Enforcement, Interior. ACTION: Final rule; approval of amendment. SUMMARY: We, the Office of Surface Mining Reclamation and Enforcement (OSM), are approving an amendment to the Texas...

76 FR 40649 - Indiana Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-11

... at 312 IAC 25-6-30 Surface mining; explosives; general requirements. The full text of the program... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 914... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period on proposed...

78 FR 10512 - Wyoming Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-14

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 950... Mining Reclamation and Enforcement, Interior. ACTION: Final rule; approval of amendment with certain... ``Wyoming program'') under the Surface Mining Control and Reclamation Act of 1977 (``SMCRA'' or ``the Act...

77 FR 8144 - Texas Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2012-02-14

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 943... AGENCY: Office of Surface Mining Reclamation and Enforcement, Interior. ACTION: Final rule; approval of amendment. SUMMARY: We, the Office of Surface Mining Reclamation and Enforcement (OSM), are approving three...

78 FR 9807 - Utah Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-12

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 944... Mining Reclamation and Enforcement, Interior. ACTION: Final rule; approval of amendment. SUMMARY: We are approving an amendment to the Utah regulatory program (the ``Utah program'') under the Surface Mining...

76 FR 30008 - Alabama Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-24

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 901... Mining Reclamation and Enforcement, Interior. ACTION: Final rule; approval of amendment. SUMMARY: We, the Office of Surface Mining Reclamation and Enforcement (OSM), are approving an amendment to the Alabama...

75 FR 43476 - Montana Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2010-07-26

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 926... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; reopening and extension of public...'') under the Surface Mining Control and Reclamation Act of 1977 (``SMCRA'' or ``the Act''). Montana revised...

75 FR 81122 - Texas Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-27

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 943... Mining Reclamation and Enforcement, Interior. ACTION: Final rule; approval of amendment. SUMMARY: We, the Office of Surface Mining Reclamation and Enforcement (OSM), are approving an amendment to the Texas...

77 FR 58025 - Texas Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2012-09-19

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 943... Mining Reclamation and Enforcement, Interior. ACTION: Final rule; approval of amendment. SUMMARY: We, the Office of Surface Mining Reclamation and Enforcement (OSM), are approving an amendment to the Texas...

An improved association-mining research for exploring Chinese herbal property theory: based on data of the Shennong's Classic of Materia Medica.

PubMed

Jin, Rui; Lin, Zhi-jian; Xue, Chun-miao; Zhang, Bing

2013-09-01

Knowledge Discovery in Databases is gaining attention and raising new hopes for traditional Chinese medicine (TCM) researchers. It is a useful tool in understanding and deciphering TCM theories. Aiming for a better understanding of Chinese herbal property theory (CHPT), this paper performed an improved association rule learning to analyze semistructured text in the book entitled Shennong's Classic of Materia Medica. The text was firstly annotated and transformed to well-structured multidimensional data. Subsequently, an Apriori algorithm was employed for producing association rules after the sensitivity analysis of parameters. From the confirmed 120 resulting rules that described the intrinsic relationships between herbal property (qi, flavor and their combinations) and herbal efficacy, two novel fundamental principles underlying CHPT were acquired and further elucidated: (1) the many-to-one mapping of herbal efficacy to herbal property; (2) the nonrandom overlap between the related efficacy of qi and flavor. This work provided an innovative knowledge about CHPT, which would be helpful for its modern research.

76 FR 25277 - Examinations of Work Areas in Underground Coal Mines and Pattern of Violations

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-04

..., 1219-AB73 Examinations of Work Areas in Underground Coal Mines and Pattern of Violations AGENCY: Mine... four public hearings on the Agency's proposed rules for Examinations of Work Areas in Underground Coal... 1219-AB75'' for Examinations of Work Areas in Underground Coal Mines' submissions, and with ``RIN 1219...

78 FR 49079 - Lease Modifications, Lease and Logical Mining Unit Diligence, Advance Royalty, Royalty Rates, and...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-12

... Management 43 CFR Parts 3000, 3400, 3430, et al. Lease Modifications, Lease and Logical Mining Unit Diligence... Lease Modifications, Lease and Logical Mining Unit Diligence, Advance Royalty, Royalty Rates, and Bonds... leases and logical mining units (LMUs). The proposed rule would implement Title IV, Subtitle D of the...

A Swarm Optimization approach for clinical knowledge mining.

PubMed

Christopher, J Jabez; Nehemiah, H Khanna; Kannan, A

2015-10-01

Rule-based classification is a typical data mining task that is being used in several medical diagnosis and decision support systems. The rules stored in the rule base have an impact on classification efficiency. Rule sets that are extracted with data mining tools and techniques are optimized using heuristic or meta-heuristic approaches in order to improve the quality of the rule base. In this work, a meta-heuristic approach called Wind-driven Swarm Optimization (WSO) is used. The uniqueness of this work lies in the biological inspiration that underlies the algorithm. WSO uses Jval, a new metric, to evaluate the efficiency of a rule-based classifier. Rules are extracted from decision trees. WSO is used to obtain different permutations and combinations of rules whereby the optimal ruleset that satisfies the requirement of the developer is used for predicting the test data. The performance of various extensions of decision trees, namely, RIPPER, PART, FURIA and Decision Tables are analyzed. The efficiency of WSO is also compared with the traditional Particle Swarm Optimization. Experiments were carried out with six benchmark medical datasets. The traditional C4.5 algorithm yields 62.89% accuracy with 43 rules for liver disorders dataset where as WSO yields 64.60% with 19 rules. For Heart disease dataset, C4.5 is 68.64% accurate with 98 rules where as WSO is 77.8% accurate with 34 rules. The normalized standard deviation for accuracy of PSO and WSO are 0.5921 and 0.5846 respectively. WSO provides accurate and concise rulesets. PSO yields results similar to that of WSO but the novelty of WSO lies in its biological motivation and it is customization for rule base optimization. The trade-off between the prediction accuracy and the size of the rule base is optimized during the design and development of rule-based clinical decision support system. The efficiency of a decision support system relies on the content of the rule base and classification accuracy. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Knowledge-guided mutation in classification rules for autism treatment efficacy.

PubMed

Engle, Kelley; Rada, Roy

2017-03-01

Data mining methods in biomedical research might benefit by combining genetic algorithms with domain-specific knowledge. The objective of this research is to show how the evolution of treatment rules for autism might be guided. The semantic distance between two concepts in the taxonomy is measured by the number of relationships separating the concepts in the taxonomy. The hypothesis is that replacing a concept in a treatment rule will change the accuracy of the rule in direct proportion to the semantic distance between the concepts. The method uses a patient database and autism taxonomies. Treatment rules are developed with an algorithm that exploits the taxonomies. The results support the hypothesis. This research should both advance the understanding of autism data mining in particular and of knowledge-guided evolutionary search in biomedicine in general.

Effective and efficient analysis of spatio-temporal data

NASA Astrophysics Data System (ADS)

Zhang, Zhongnan

Spatio-temporal data mining, i.e., mining knowledge from large amount of spatio-temporal data, is a highly demanding field because huge amounts of spatio-temporal data have been collected in various applications, ranging from remote sensing, to geographical information systems (GIS), computer cartography, environmental assessment and planning, etc. The collection data far exceeded human's ability to analyze which make it crucial to develop analysis tools. Recent studies on data mining have extended to the scope of data mining from relational and transactional datasets to spatial and temporal datasets. Among the various forms of spatio-temporal data, remote sensing images play an important role, due to the growing wide-spreading of outer space satellites. In this dissertation, we proposed two approaches to analyze the remote sensing data. The first one is about applying association rules mining onto images processing. Each image was divided into a number of image blocks. We built a spatial relationship for these blocks during the dividing process. This made a large number of images into a spatio-temporal dataset since each image was shot in time-series. The second one implemented co-occurrence patterns discovery from these images. The generated patterns represent subsets of spatial features that are located together in space and time. A weather analysis is composed of individual analysis of several meteorological variables. These variables include temperature, pressure, dew point, wind, clouds, visibility and so on. Local-scale models provide detailed analysis and forecasts of meteorological phenomena ranging from a few kilometers to about 100 kilometers in size. When some of above meteorological variables have some special change tendency, some kind of severe weather will happen in most cases. Using the discovery of association rules, we found that some special meteorological variables' changing has tight relation with some severe weather situation that will happen very soon. This dissertation is composed of three parts: an introduction, some basic knowledges and relative works, and my own three contributions to the development of approaches for spatio-temporal data mining: DYSTAL algorithm, STARSI algorithm, and COSTCOP+ algorithm.

76 FR 64048 - Pennsylvania Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-17

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 938... Surface Mining Reclamation and Enforcement (OSM), Interior. ACTION: Proposed rule; reopening and extension... Mining Control and Reclamation Act of 1977 (SMCRA or the Act) published on February 7, 2011. In response...

30 CFR 301.1 - Cross reference.

Code of Federal Regulations, 2010 CFR

2010-07-01

... within the jurisdiction of administrative law judges and the Interior Board of Surface Mining and... Resources BOARD OF SURFACE MINING AND RECLAMATION APPEALS, DEPARTMENT OF THE INTERIOR PROCEDURES UNDER SURFACE MINING CONTROL AND RECLAMATION ACT OF 1977 § 301.1 Cross reference. For special rules applicable...

75 FR 60271 - Technical Amendments 2010

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-29

... Part VI Department of the Interior Office of Surface Mining Reclamation and Enforcement 30 CFR... INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Parts 740, 761, 773, 795, 816, 817...: Office of Surface Mining Reclamation and Enforcement, Interior. ACTION: Final rule. SUMMARY: We, the...

30 CFR 921.700 - Massachusetts Federal program.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 921.700 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE MASSACHUSETTS § 921.700 Massachusetts Federal program. (a) This part contains all rules that are applicable to surface coal mining...

77 FR 58053 - Kentucky Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2012-09-19

... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 917... Mining Reclamation and Enforcement (OSM), Interior. ACTION: Proposed rule; Removal of Required Amendments... program'') under the Surface Mining Control and Reclamation Act of 1977 (SMCRA or the Act). As a result of...

30 CFR 937.700 - Oregon Federal program.

Code of Federal Regulations, 2013 CFR

2013-07-01

... Federal program. (c) The rules in this part apply to all surface coal mining operations in Oregon... more stringent environmental control and regulation of surface coal mining operations than do the... extent they provide for regulation of surface coal mining and reclamation operations which are exempt...

30 CFR 912.700 - Idaho Federal program.

Code of Federal Regulations, 2011 CFR

2011-07-01

... seq. and Rules 1 through 20 promulgated thereunder pertaining to regulation of dredge mining. (6... Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE IDAHO § 912.700 Idaho Federal...

75 FR 64411 - Lowering Miners' Exposure to Respirable Coal Mine Dust, Including Continuous Personal Dust Monitors

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-19

...The Mine Safety and Health Administration (MSHA) proposes to lower miners' exposure to respirable coal mine dust by revising the Agency's existing standards on miners' occupational exposure to respirable coal mine dust. The major provisions of the proposal would lower the existing exposure limit; provide for full-shift sampling; redefine the term ``normal production shift; '' and add reexamination and decertification requirements for persons certified to sample, and maintain and calibrate sampling devices. In addition, the proposed rule would provide for single shift compliance sampling under the mine operator and MSHA's inspector sampling programs, and would establish sampling requirements for use of the Continuous Personal Dust Monitor (CPDM) and expanded requirements for medical surveillance. The proposed rule would significantly improve health protections for this Nation's coal miners by reducing their occupational exposure to respirable coal mine dust and lowering the risk that they will suffer material impairment of health or functional capacity over their working lives.

Assessing Lightning and Wildfire Hazard by Land Properties and Cloud to Ground Lightning Data with Association Rule Mining in Alberta, Canada

PubMed Central

Cha, DongHwan; Wang, Xin; Kim, Jeong Woo

2017-01-01

Hotspot analysis was implemented to find regions in the province of Alberta (Canada) with high frequency Cloud to Ground (CG) lightning strikes clustered together. Generally, hotspot regions are located in the central, central east, and south central regions of the study region. About 94% of annual lightning occurred during warm months (June to August) and the daily lightning frequency was influenced by the diurnal heating cycle. The association rule mining technique was used to investigate frequent CG lightning patterns, which were verified by similarity measurement to check the patterns’ consistency. The similarity coefficient values indicated that there were high correlations throughout the entire study period. Most wildfires (about 93%) in Alberta occurred in forests, wetland forests, and wetland shrub areas. It was also found that lightning and wildfires occur in two distinct areas: frequent wildfire regions with a high frequency of lightning, and frequent wild-fire regions with a low frequency of lightning. Further, the preference index (PI) revealed locations where the wildfires occurred more frequently than in other class regions. The wildfire hazard area was estimated with the CG lightning hazard map and specific land use types. PMID:29065564

Assessing Lightning and Wildfire Hazard by Land Properties and Cloud to Ground Lightning Data with Association Rule Mining in Alberta, Canada.

PubMed

Cha, DongHwan; Wang, Xin; Kim, Jeong Woo

2017-10-23

Hotspot analysis was implemented to find regions in the province of Alberta (Canada) with high frequency Cloud to Ground (CG) lightning strikes clustered together. Generally, hotspot regions are located in the central, central east, and south central regions of the study region. About 94% of annual lightning occurred during warm months (June to August) and the daily lightning frequency was influenced by the diurnal heating cycle. The association rule mining technique was used to investigate frequent CG lightning patterns, which were verified by similarity measurement to check the patterns' consistency. The similarity coefficient values indicated that there were high correlations throughout the entire study period. Most wildfires (about 93%) in Alberta occurred in forests, wetland forests, and wetland shrub areas. It was also found that lightning and wildfires occur in two distinct areas: frequent wildfire regions with a high frequency of lightning, and frequent wild-fire regions with a low frequency of lightning. Further, the preference index (PI) revealed locations where the wildfires occurred more frequently than in other class regions. The wildfire hazard area was estimated with the CG lightning hazard map and specific land use types.

76 FR 41411 - West Virginia Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-14

... of Environmental Protection (WVDEP). The interim rule provided an opportunity for public comment and... 30 CFR Part 948 Intergovernmental relations, Surface mining, Underground mining. Dated: July 5, 2011...

ConGEMs: Condensed Gene Co-Expression Module Discovery Through Rule-Based Clustering and Its Application to Carcinogenesis.

PubMed

Mallik, Saurav; Zhao, Zhongming

2017-12-28

For transcriptomic analysis, there are numerous microarray-based genomic data, especially those generated for cancer research. The typical analysis measures the difference between a cancer sample-group and a matched control group for each transcript or gene. Association rule mining is used to discover interesting item sets through rule-based methodology. Thus, it has advantages to find causal effect relationships between the transcripts. In this work, we introduce two new rule-based similarity measures-weighted rank-based Jaccard and Cosine measures-and then propose a novel computational framework to detect condensed gene co-expression modules ( C o n G E M s) through the association rule-based learning system and the weighted similarity scores. In practice, the list of evolved condensed markers that consists of both singular and complex markers in nature depends on the corresponding condensed gene sets in either antecedent or consequent of the rules of the resultant modules. In our evaluation, these markers could be supported by literature evidence, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway and Gene Ontology annotations. Specifically, we preliminarily identified differentially expressed genes using an empirical Bayes test. A recently developed algorithm-RANWAR-was then utilized to determine the association rules from these genes. Based on that, we computed the integrated similarity scores of these rule-based similarity measures between each rule-pair, and the resultant scores were used for clustering to identify the co-expressed rule-modules. We applied our method to a gene expression dataset for lung squamous cell carcinoma and a genome methylation dataset for uterine cervical carcinogenesis. Our proposed module discovery method produced better results than the traditional gene-module discovery measures. In summary, our proposed rule-based method is useful for exploring biomarker modules from transcriptomic data.

30 CFR 912.700 - Idaho Federal program.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE IDAHO § 912.700 Idaho Federal program. (a) This part contains all rules that are applicable to surface coal mining operations in Idaho...

30 CFR 905.700 - California Federal Program.

Code of Federal Regulations, 2010 CFR

2010-07-01

....700 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE CALIFORNIA § 905.700 California Federal Program. (a) This part contains all rules that are applicable to surface coal mining operations in...

30 CFR 947.700 - Washington Federal program.

Code of Federal Regulations, 2010 CFR

2010-07-01

....700 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE WASHINGTON § 947.700 Washington Federal program. (a) This part contains all rules that are applicable to surface coal mining operations in...

30 CFR 922.700 - Michigan Federal program.

Code of Federal Regulations, 2010 CFR

2010-07-01

....700 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE MICHIGAN § 922.700 Michigan Federal program. (a) This part contains all rules that are applicable to surface coal mining operations in...

30 CFR 910.700 - Georgia Federal program.

Code of Federal Regulations, 2010 CFR

2010-07-01

....700 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE GEORGIA § 910.700 Georgia Federal program. (a) This part contains all rules that are applicable to surface coal mining operations in Georgia...

30 CFR 937.700 - Oregon Federal program.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE OREGON § 937.700 Oregon Federal program. (a) This part contains all rules that are applicable to surface coal mining operations in Oregon...

30 CFR 942.700 - Tennessee Federal program.

Code of Federal Regulations, 2010 CFR

2010-07-01

....700 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE TENNESSEE § 942.700 Tennessee Federal program. (a) This part contains all rules that are applicable to surface coal mining operations in...

Performance of Case-Based Reasoning Retrieval Using Classification Based on Associations versus Jcolibri and FreeCBR: A Further Validation Study

NASA Astrophysics Data System (ADS)

Aljuboori, Ahmed S.; Coenen, Frans; Nsaif, Mohammed; Parsons, David J.

2018-05-01

Case-Based Reasoning (CBR) plays a major role in expert system research. However, a critical problem can be met when a CBR system retrieves incorrect cases. Class Association Rules (CARs) have been utilized to offer a potential solution in a previous work. The aim of this paper was to perform further validation of Case-Based Reasoning using a Classification based on Association Rules (CBRAR) to enhance the performance of Similarity Based Retrieval (SBR). The CBRAR strategy uses a classed frequent pattern tree algorithm (FP-CAR) in order to disambiguate wrongly retrieved cases in CBR. The research reported in this paper makes contributions to both fields of CBR and Association Rules Mining (ARM) in that full target cases can be extracted from the FP-CAR algorithm without invoking P-trees and union operations. The dataset used in this paper provided more efficient results when the SBR retrieves unrelated answers. The accuracy of the proposed CBRAR system outperforms the results obtained by existing CBR tools such as Jcolibri and FreeCBR.

Discovery of novel targets for multi-epitope vaccines: Screening of HIV-1 genomes using association rule mining

PubMed Central

Paul, Sinu; Piontkivska, Helen

2009-01-01

Background Studies have shown that in the genome of human immunodeficiency virus (HIV-1) regions responsible for interactions with the host's immune system, namely, cytotoxic T-lymphocyte (CTL) epitopes tend to cluster together in relatively conserved regions. On the other hand, "epitope-less" regions or regions with relatively low density of epitopes tend to be more variable. However, very little is known about relationships among epitopes from different genes, in other words, whether particular epitopes from different genes would occur together in the same viral genome. To identify CTL epitopes in different genes that co-occur in HIV genomes, association rule mining was used. Results Using a set of 189 best-defined HIV-1 CTL/CD8+ epitopes from 9 different protein-coding genes, as described by Frahm, Linde & Brander (2007), we examined the complete genomic sequences of 62 reference HIV sequences (including 13 subtypes and sub-subtypes with approximately 4 representative sequences for each subtype or sub-subtype, and 18 circulating recombinant forms). The results showed that despite inclusion of recombinant sequences that would be expected to break-up associations of epitopes in different genes when two different genomes are recombined, there exist particular combinations of epitopes (epitope associations) that occur repeatedly across the world-wide population of HIV-1. For example, Pol epitope LFLDGIDKA is found to be significantly associated with epitopes GHQAAMQML and FLKEKGGL from Gag and Nef, respectively, and this association rule is observed even among circulating recombinant forms. Conclusion We have identified CTL epitope combinations co-occurring in HIV-1 genomes including different subtypes and recombinant forms. Such co-occurrence has important implications for design of complex vaccines (multi-epitope vaccines) and/or drugs that would target multiple HIV-1 regions at once and, thus, may be expected to overcome challenges associated with viral escape. PMID:19580659

Object-Driven and Temporal Action Rules Mining

ERIC Educational Resources Information Center

Hajja, Ayman

2013-01-01

In this thesis, I present my complete research work in the field of action rules, more precisely object-driven and temporal action rules. The drive behind the introduction of object-driven and temporally based action rules is to bring forth an adapted approach to extract action rules from a subclass of systems that have a specific nature, in which…

Finding novel relationships with integrated gene-gene association network analysis of Synechocystis sp. PCC 6803 using species-independent text-mining.

PubMed

Kreula, Sanna M; Kaewphan, Suwisa; Ginter, Filip; Jones, Patrik R

2018-01-01

The increasing move towards open access full-text scientific literature enhances our ability to utilize advanced text-mining methods to construct information-rich networks that no human will be able to grasp simply from 'reading the literature'. The utility of text-mining for well-studied species is obvious though the utility for less studied species, or those with no prior track-record at all, is not clear. Here we present a concept for how advanced text-mining can be used to create information-rich networks even for less well studied species and apply it to generate an open-access gene-gene association network resource for Synechocystis sp. PCC 6803, a representative model organism for cyanobacteria and first case-study for the methodology. By merging the text-mining network with networks generated from species-specific experimental data, network integration was used to enhance the accuracy of predicting novel interactions that are biologically relevant. A rule-based algorithm (filter) was constructed in order to automate the search for novel candidate genes with a high degree of likely association to known target genes by (1) ignoring established relationships from the existing literature, as they are already 'known', and (2) demanding multiple independent evidences for every novel and potentially relevant relationship. Using selected case studies, we demonstrate the utility of the network resource and filter to ( i ) discover novel candidate associations between different genes or proteins in the network, and ( ii ) rapidly evaluate the potential role of any one particular gene or protein. The full network is provided as an open-source resource.

Discovering amino acid patterns on binding sites in protein complexes

PubMed Central

Kuo, Huang-Cheng; Ong, Ping-Lin; Lin, Jung-Chang; Huang, Jen-Peng

2011-01-01

Discovering amino acid (AA) patterns on protein binding sites has recently become popular. We propose a method to discover the association relationship among AAs on binding sites. Such knowledge of binding sites is very helpful in predicting protein-protein interactions. In this paper, we focus on protein complexes which have protein-protein recognition. The association rule mining technique is used to discover geographically adjacent amino acids on a binding site of a protein complex. When mining, instead of treating all AAs of binding sites as a transaction, we geographically partition AAs of binding sites in a protein complex. AAs in a partition are treated as a transaction. For the partition process, AAs on a binding site are projected from three-dimensional to two-dimensional. And then, assisted with a circular grid, AAs on the binding site are placed into grid cells. A circular grid has ten rings: a central ring, the second ring with 6 sectors, the third ring with 12 sectors, and later rings are added to four sectors in order. As for the radius of each ring, we examined the complexes and found that 10Å is a suitable range, which can be set by the user. After placing these recognition complexes on the circular grid, we obtain mining records (i.e. transactions) from each sector. A sector is regarded as a record. Finally, we use the association rule to mine these records for frequent AA patterns. If the support of an AA pattern is larger than the predetermined minimum support (i.e. threshold), it is called a frequent pattern. With these discovered patterns, we offer the biologists a novel point of view, which will improve the prediction accuracy of protein-protein recognition. In our experiments, we produced the AA patterns by data mining. As a result, we found that arginine (arg) most frequently appears on the binding sites of two proteins in the recognition protein complexes, while cysteine (cys) appears the fewest. In addition, if we discriminate the shape of binding sites between concave and convex further, we discover that patterns {arg, glu, asp} and {arg, ser, asp} on the concave shape of binding sites in a protein more frequently (i.e. higher probability) make contact with {lys} or {arg} on the convex shape of binding sites in another protein. Thus, we can confidently achieve a rate of at least 78%. On the other hand {val, gly, lys} on the convex surface of binding sites in proteins is more frequently in contact with {asp} on the concave site of another protein, and the confidence achieved is over 81%. Applying data mining in biology can reveal more facts that may otherwise be ignored or not easily discovered by the naked eye. Furthermore, we can discover more relationships among AAs on binding sites by appropriately rotating these residues on binding sites from a three-dimension to two-dimension perspective. We designed a circular grid to deposit the data, which total to 463 records consisting of AAs. Then we used the association rules to mine these records for discovering relationships. The proposed method in this paper provides an insight into the characteristics of binding sites for recognition complexes. PMID:21464838

Evolutionary Data Mining Approach to Creating Digital Logic

DTIC Science & Technology

2010-01-01

To deal with this problem a genetic program (GP) based data mining ( DM ) procedure has been invented (Smith 2005). A genetic program is an algorithm...that can operate on the variables. When a GP was used as a DM function in the past to automatically create fuzzy decision trees, the Report...rules represents an approach to the determining the effect of linguistic imprecision, i.e., the inability of experts to provide crisp rules. The

Genetic Algorithm Calibration of Probabilistic Cellular Automata for Modeling Mining Permit Activity

USGS Publications Warehouse

Louis, S.J.; Raines, G.L.

2003-01-01

We use a genetic algorithm to calibrate a spatially and temporally resolved cellular automata to model mining activity on public land in Idaho and western Montana. The genetic algorithm searches through a space of transition rule parameters of a two dimensional cellular automata model to find rule parameters that fit observed mining activity data. Previous work by one of the authors in calibrating the cellular automaton took weeks - the genetic algorithm takes a day and produces rules leading to about the same (or better) fit to observed data. These preliminary results indicate that genetic algorithms are a viable tool in calibrating cellular automata for this application. Experience gained during the calibration of this cellular automata suggests that mineral resource information is a critical factor in the quality of the results. With automated calibration, further refinements of how the mineral-resource information is provided to the cellular automaton will probably improve our model.

Rescue complex for coal mines

NASA Astrophysics Data System (ADS)

Yungmeyster, D. A.; Urazbakhtin, R. Yu

2017-10-01

The mining industry was potentially dangerous at all times, even with the use of modern equipment in mines, accidents continue to occur, including catastrophic ones. Accidents in mines are due to the presence of specific features in the conduct of mining operations. These include the inconsistency of mining and geological conditions, the contamination of the mine atmosphere due to the release of gases from minerals, the presence of self-igniting coal strata, which creates the danger of underground fires, gas explosions. The main cause of accidents is the irresponsibility of both the manager and the personnel who violate the safety rules during mining operations.

Analysis of Human Mobility Based on Cellular Data

NASA Astrophysics Data System (ADS)

Arifiansyah, F.; Saptawati, G. A. P.

2017-01-01

Nowadays not only adult but even teenager and children have then own mobile phones. This phenomena indicates that the mobile phone becomes an important part of everyday’s life. Based on these indication, the amount of cellular data also increased rapidly. Cellular data defined as the data that records communication among mobile phone users. Cellular data is easy to obtain because the telecommunications company had made a record of the data for the billing system of the company. Billing data keeps a log of the users cellular data usage each time. We can obtained information from the data about communication between users. Through data visualization process, an interesting pattern can be seen in the raw cellular data, so that users can obtain prior knowledge to perform data analysis. Cellular data processing can be done using data mining to find out human mobility patterns and on the existing data. In this paper, we use frequent pattern mining and finding association rules to observe the relation between attributes in cellular data and then visualize them. We used weka tools for finding the rules in stage of data mining. Generally, the utilization of cellular data can provide supporting information for the decision making process and become a data support to provide solutions and information needed by the decision makers.

Literature evidence in open targets - a target validation platform.

PubMed

Kafkas, Şenay; Dunham, Ian; McEntyre, Johanna

2017-06-06

We present the Europe PMC literature component of Open Targets - a target validation platform that integrates various evidence to aid drug target identification and validation. The component identifies target-disease associations in documents and ranks the documents based on their confidence from the Europe PMC literature database, by using rules utilising expert-provided heuristic information. The confidence score of a given document represents how valuable the document is in the scope of target validation for a given target-disease association by taking into account the credibility of the association based on the properties of the text. The component serves the platform regularly with the up-to-date data since December, 2015. Currently, there are a total number of 1168365 distinct target-disease associations text mined from >26 million PubMed abstracts and >1.2 million Open Access full text articles. Our comparative analyses on the current available evidence data in the platform revealed that 850179 of these associations are exclusively identified by literature mining. This component helps the platform's users by providing the most relevant literature hits for a given target and disease. The text mining evidence along with the other types of evidence can be explored visually through https://www.targetvalidation.org and all the evidence data is available for download in json format from https://www.targetvalidation.org/downloads/data .

Evaluation of rational nonsteroidal anti-inflammatory drugs and gastro-protective agents use; association rule data mining using outpatient prescription patterns.

PubMed

Pattanaprateep, Oraluck; McEvoy, Mark; Attia, John; Thakkinstian, Ammarin

2017-07-04

Nonsteroidal anti-inflammatory drugs (NSAIDs) and gastro-protective agents should be co-prescribed following a standard clinical practice guideline; however, adherence to this guideline in routine practice is unknown. This study applied an association rule model (ARM) to estimate rational NSAIDs and gastro-protective agents use in an outpatient prescriptions dataset. A database of hospital outpatients from October 1st, 2013 to September 30th, 2015 was searched for any of following drugs: oral antacids (A02A), peptic ulcer and gastro-oesophageal reflux disease drugs (GORD, A02B), and anti-inflammatory and anti-rheumatic products, non-steroids or NSAIDs (M01A). Data including patient demographics, diagnoses, and drug utilization were also retrieved. An association rule model was used to analyze co-prescription of the same drug class (i.e., prescriptions within A02A-A02B, M01A) and between drug classes (A02A-A02B & M01A) using the Apriori algorithm in R. The lift value, was calculated by a ratio of confidence to expected confidence, which gave information about the association between drugs in the prescription. We identified a total of 404,273 patients with 2,575,331 outpatient visits in 2 fiscal years. Mean age was 48 years and 34% were male. Among A02A, A02B and M01A drug classes, 12 rules of associations were discovered with support and confidence thresholds of 1% and 50%. The highest lift was between Omeprazole and Ranitidine (340 visits); about one-third of these visits (118) were prescriptions to non-GORD patients, contrary to guidelines. Another finding was the concomitant use of COX-2 inhibitors (Etoricoxib or Celecoxib) and PPIs. 35.6% of these were for patients aged less than 60 years with no GI complication and no Aspirin, inconsistent with guidelines. Around one-third of occasions where these medications were co-prescribed were inconsistent with guidelines. With the rapid growth of health datasets, data mining methods may help assess quality of care and concordance with guidelines and best evidence.

Short-term effects of air pollution on lower respiratory diseases and forecasting by the group method of data handling

NASA Astrophysics Data System (ADS)

Zhu, Wenjin; Wang, Jianzhou; Zhang, Wenyu; Sun, Donghuai

2012-05-01

Risk of lower respiratory diseases was significantly correlated with levels of monthly average concentration of SO2; NO2 and association rules have high lifts. In view of Lanzhou's special geographical location, taking into account the impact of different seasons, especially for the winter, the relations between air pollutants and the respiratory disease deserve further study. In this study the monthly average concentration of SO2, NO2, PM10 and the monthly number of people who in hospital because of lower respiratory disease from January 2001 to December 2005 are grouped equidistant and considered as the terms of transactions. Then based on the relational algebraic theory we employed the optimization relation association rule to mine the association rules of the transactions. Based on the association rules revealing the effects of air pollutants on the lower respiratory disease, we forecast the number of person who suffered from lower respiratory disease by the group method of data handling (GMDH) to reveal the risk and give a consultation to the hospital in Xigu District, the most seriously polluted district in Lanzhou. The data and analysis indicate that individuals may be susceptible to the short-term effects of pollution and thus suffer from lower respiratory diseases and this effect presents seasonal.

Mining association rule based on the diseases population for recommendation of medicine need

NASA Astrophysics Data System (ADS)

Harahap, M.; Husein, A. M.; Aisyah, S.; Lubis, F. R.; Wijaya, B. A.

2018-04-01

Selection of medicines that is inappropriate will lead to an empty result at medicines, this has an impact on medical services and economic value in hospital. The importance of an appropriate medicine selection process requires an automated way to select need based on the development of the patient's illness. In this study, we analyzed patient prescriptions to identify the relationship between the disease and the medicine used by the physician in treating the patient's illness. The analytical framework includes: (1) patient prescription data collection, (2) applying k-means clustering to classify the top 10 diseases, (3) applying Apriori algorithm to find association rules based on support, confidence and lift value. The results of the tests of patient prescription datasets in 2015-2016, the application of the k-means algorithm for the clustering of 10 dominant diseases significantly affects the value of trust and support of all association rules on the Apriori algorithm making it more consistent with finding association rules of disease and related medicine. The value of support, confidence and the lift value of disease and related medicine can be used as recommendations for appropriate medicine selection. Based on the conditions of disease progressions of the hospital, there is so more optimal medicine procurement.

Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer's disease patients.

PubMed

Mihelčić, Matej; Šimić, Goran; Babić Leko, Mirjana; Lavrač, Nada; Džeroski, Sašo; Šmuc, Tomislav

2017-01-01

Based on a set of subjects and a collection of attributes obtained from the Alzheimer's Disease Neuroimaging Initiative database, we used redescription mining to find interpretable rules revealing associations between those determinants that provide insights about the Alzheimer's disease (AD). We extended the CLUS-RM redescription mining algorithm to a constraint-based redescription mining (CBRM) setting, which enables several modes of targeted exploration of specific, user-constrained associations. Redescription mining enabled finding specific constructs of clinical and biological attributes that describe many groups of subjects of different size, homogeneity and levels of cognitive impairment. We confirmed some previously known findings. However, in some instances, as with the attributes: testosterone, ciliary neurotrophic factor, brain natriuretic peptide, Fas ligand, the imaging attribute Spatial Pattern of Abnormalities for Recognition of Early AD, as well as the levels of leptin and angiopoietin-2 in plasma, we corroborated previously debatable findings or provided additional information about these variables and their association with AD pathogenesis. Moreover, applying redescription mining on ADNI data resulted with the discovery of one largely unknown attribute: the Pregnancy-Associated Protein-A (PAPP-A), which we found highly associated with cognitive impairment in AD. Statistically significant correlations (p ≤ 0.01) were found between PAPP-A and clinical tests: Alzheimer's Disease Assessment Scale, Clinical Dementia Rating Sum of Boxes, Mini Mental State Examination, etc. The high importance of this finding lies in the fact that PAPP-A is a metalloproteinase, known to cleave insulin-like growth factor binding proteins. Since it also shares similar substrates with A Disintegrin and the Metalloproteinase family of enzymes that act as α-secretase to physiologically cleave amyloid precursor protein (APP) in the non-amyloidogenic pathway, it could be directly involved in the metabolism of APP very early during the disease course. Therefore, further studies should investigate the role of PAPP-A in the development of AD more thoroughly.

Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer’s disease patients

PubMed Central

Mihelčić, Matej; Šimić, Goran; Babić Leko, Mirjana; Lavrač, Nada; Džeroski, Sašo; Šmuc, Tomislav

2017-01-01

Based on a set of subjects and a collection of attributes obtained from the Alzheimer’s Disease Neuroimaging Initiative database, we used redescription mining to find interpretable rules revealing associations between those determinants that provide insights about the Alzheimer’s disease (AD). We extended the CLUS-RM redescription mining algorithm to a constraint-based redescription mining (CBRM) setting, which enables several modes of targeted exploration of specific, user-constrained associations. Redescription mining enabled finding specific constructs of clinical and biological attributes that describe many groups of subjects of different size, homogeneity and levels of cognitive impairment. We confirmed some previously known findings. However, in some instances, as with the attributes: testosterone, ciliary neurotrophic factor, brain natriuretic peptide, Fas ligand, the imaging attribute Spatial Pattern of Abnormalities for Recognition of Early AD, as well as the levels of leptin and angiopoietin-2 in plasma, we corroborated previously debatable findings or provided additional information about these variables and their association with AD pathogenesis. Moreover, applying redescription mining on ADNI data resulted with the discovery of one largely unknown attribute: the Pregnancy-Associated Protein-A (PAPP-A), which we found highly associated with cognitive impairment in AD. Statistically significant correlations (p ≤ 0.01) were found between PAPP-A and clinical tests: Alzheimer’s Disease Assessment Scale, Clinical Dementia Rating Sum of Boxes, Mini Mental State Examination, etc. The high importance of this finding lies in the fact that PAPP-A is a metalloproteinase, known to cleave insulin-like growth factor binding proteins. Since it also shares similar substrates with A Disintegrin and the Metalloproteinase family of enzymes that act as α-secretase to physiologically cleave amyloid precursor protein (APP) in the non-amyloidogenic pathway, it could be directly involved in the metabolism of APP very early during the disease course. Therefore, further studies should investigate the role of PAPP-A in the development of AD more thoroughly. PMID:29088293

NIOSH comments to DOL on the Mine Safety and Health Administration's proposed rule on air quality, chemical substances, and respiratory protection standards by J. D. Millar, March 1, 1990

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

The testimony concerns the views of NIOSH regarding the Mine Safety and Health Administration (MSHA) proposed rule on permissible exposure limits; exposure monitoring, abrasive blasting; drill dust control; dangerous atmospheres; and prohibited areas for food and beverages. NIOSH continues to endorse the recommended exposure limit of 1 part per million (ppm) as a 15 minute short term exposure limit for nitrogen-dioxide (10102440). NIOSH supports MSHA in proposing an 8 hour time weighted average of 25ppm for nitric-oxide (10102439). NIOSH supports MSHA in proposing a limit of 35ppm as an 8 hour time weighted average (TWA) for carbon-monoxide (630080) and recommendsmore » that sulfur-dioxide (7446095) exposure be limited to 0.5ppm as an 8 hour TWA. NIOSH recommends that routine air monitoring be required on a periodic basis. NIOSH recommends that mine operators be required to establish a written exposure monitoring plan for each facility that outlines where area and personal samples should be taken, how many samples should be taken, and the implementation of the remaining portions of the proposed rule change. NIOSH supports the rules for abrasive blasting for both coal and metal/nonmetal mines and has identified several substitutive materials for silica sand that could be used in abrasive blasting.« less

30 CFR 77.1600 - Loading and haulage; general.

Code of Federal Regulations, 2014 CFR

2014-07-01

... permitted on haulage roads and at loading or dumping locations. (b) Traffic rules, signals, and warning signs shall be standardized at each mine and posted. (c) Where side or overhead clearances on any haulage road or at any loading or dumping location at the mine are hazardous to mine workers, such areas...

30 CFR 77.1600 - Loading and haulage; general.

Code of Federal Regulations, 2012 CFR

2012-07-01

... permitted on haulage roads and at loading or dumping locations. (b) Traffic rules, signals, and warning signs shall be standardized at each mine and posted. (c) Where side or overhead clearances on any haulage road or at any loading or dumping location at the mine are hazardous to mine workers, such areas...

30 CFR 77.1600 - Loading and haulage; general.

Code of Federal Regulations, 2013 CFR

2013-07-01

... permitted on haulage roads and at loading or dumping locations. (b) Traffic rules, signals, and warning signs shall be standardized at each mine and posted. (c) Where side or overhead clearances on any haulage road or at any loading or dumping location at the mine are hazardous to mine workers, such areas...

30 CFR 77.1600 - Loading and haulage; general.

Code of Federal Regulations, 2010 CFR

2010-07-01

... permitted on haulage roads and at loading or dumping locations. (b) Traffic rules, signals, and warning signs shall be standardized at each mine and posted. (c) Where side or overhead clearances on any haulage road or at any loading or dumping location at the mine are hazardous to mine workers, such areas...

30 CFR 77.1600 - Loading and haulage; general.

Code of Federal Regulations, 2011 CFR

2011-07-01

... permitted on haulage roads and at loading or dumping locations. (b) Traffic rules, signals, and warning signs shall be standardized at each mine and posted. (c) Where side or overhead clearances on any haulage road or at any loading or dumping location at the mine are hazardous to mine workers, such areas...

30 CFR 944.30 - State-Federal Cooperative Agreement.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Division of Oil, Gas, and Mining (DOGM) will be responsible for administering this Agreement on behalf of..., Final Rules of the Board of Oil, Gas and Mining, UMC/SMC 700 et seq. [52 FR 7850, Mar. 13, 1987] ... INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE UTAH § 944.30 State...

30 CFR 944.30 - State-Federal Cooperative Agreement.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Division of Oil, Gas, and Mining (DOGM) will be responsible for administering this Agreement on behalf of..., Final Rules of the Board of Oil, Gas and Mining, UMC/SMC 700 et seq. [52 FR 7850, Mar. 13, 1987] ... INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE UTAH § 944.30 State...

30 CFR 944.30 - State-Federal Cooperative Agreement.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Division of Oil, Gas, and Mining (DOGM) will be responsible for administering this Agreement on behalf of..., Final Rules of the Board of Oil, Gas and Mining, UMC/SMC 700 et seq. [52 FR 7850, Mar. 13, 1987] ... INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE UTAH § 944.30 State...

30 CFR 944.30 - State-Federal Cooperative Agreement.

Code of Federal Regulations, 2013 CFR

2013-07-01

... Division of Oil, Gas, and Mining (DOGM) will be responsible for administering this Agreement on behalf of..., Final Rules of the Board of Oil, Gas and Mining, UMC/SMC 700 et seq. [52 FR 7850, Mar. 13, 1987] ... INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE UTAH § 944.30 State...

Data Mining and Privacy of Social Network Sites' Users: Implications of the Data Mining Problem.

PubMed

Al-Saggaf, Yeslam; Islam, Md Zahidul

2015-08-01

This paper explores the potential of data mining as a technique that could be used by malicious data miners to threaten the privacy of social network sites (SNS) users. It applies a data mining algorithm to a real dataset to provide empirically-based evidence of the ease with which characteristics about the SNS users can be discovered and used in a way that could invade their privacy. One major contribution of this article is the use of the decision forest data mining algorithm (SysFor) to the context of SNS, which does not only build a decision tree but rather a forest allowing the exploration of more logic rules from a dataset. One logic rule that SysFor built in this study, for example, revealed that anyone having a profile picture showing just the face or a picture showing a family is less likely to be lonely. Another contribution of this article is the discussion of the implications of the data mining problem for governments, businesses, developers and the SNS users themselves.

Nursing Routine Data as a Basis for Association Analysis in the Domain of Nursing Knowledge

PubMed Central

Sellemann, Björn; Stausberg, Jürgen; Hübner, Ursula

2012-01-01

This paper describes the data mining method of association analysis within the framework of Knowledge Discovery in Databases (KDD) with the aim to identify standard patterns of nursing care. The approach is application-oriented and used on nursing routine data of the method LEP nursing 2. The increasing use of information technology in hospitals, especially of nursing information systems, requires the storage of large data sets, which hitherto have not always been analyzed adequately. Three association analyses for the days of admission, surgery and discharge, have been performed. The results of almost 1.5 million generated association rules indicate that it is valid to apply association analysis to nursing routine data. All rules are semantically trivial, since they reflect existing knowledge from the domain of nursing. This may be due either to the method LEP Nursing 2, or to the nursing activities themselves. Nonetheless, association analysis may in future become a useful analytical tool on the basis of structured nursing routine data. PMID:24199122

Application of Kansei engineering and data mining in the Thai ceramic manufacturing

NASA Astrophysics Data System (ADS)

Kittidecha, Chaiwat; Yamada, Koichi

2018-01-01

Ceramic is one of the highly competitive products in Thailand. Many Thai ceramic companies are attempting to know the customer needs and perceptions for making favorite products. To know customer needs is the target of designers and to develop a product that must satisfy customers. This research is applied Kansei Engineering (KE) and Data Mining (DM) into the customer driven product design process. KE can translate customer emotions into the product attributes. This method determines the relationships between customer feelings or Kansei words and the design attributes. Decision tree J48 and Class association rule which implemented through Waikato Environment for Knowledge Analysis (WEKA) software are used to generate a predictive model and to find the appropriate rules. In this experiment, the emotion scores were rated by 37 participants for training data and 16 participants for test data. 6 Kansei words were selected, namely, attractive, ease of drinking, ease of handing, quality, modern and durable. 10 mugs were selected as product samples. The results of this study indicate that the proposed models and rules can interpret the design product elements affecting the customer emotions. Finally, this study provides useful understanding for the application DM in KE and can be applied to a variety of design cases.

Applying Data Mining Techniques to Extract Hidden Patterns about Breast Cancer Survival in an Iranian Cohort Study.

PubMed

Khalkhali, Hamid Reza; Lotfnezhad Afshar, Hadi; Esnaashari, Omid; Jabbari, Nasrollah

2016-01-01

Breast cancer survival has been analyzed by many standard data mining algorithms. A group of these algorithms belonged to the decision tree category. Ability of the decision tree algorithms in terms of visualizing and formulating of hidden patterns among study variables were main reasons to apply an algorithm from the decision tree category in the current study that has not studied already. The classification and regression trees (CART) was applied to a breast cancer database contained information on 569 patients in 2007-2010. The measurement of Gini impurity used for categorical target variables was utilized. The classification error that is a function of tree size was measured by 10-fold cross-validation experiments. The performance of created model was evaluated by the criteria as accuracy, sensitivity and specificity. The CART model produced a decision tree with 17 nodes, 9 of which were associated with a set of rules. The rules were meaningful clinically. They showed in the if-then format that Stage was the most important variable for predicting breast cancer survival. The scores of accuracy, sensitivity and specificity were: 80.3%, 93.5% and 53%, respectively. The current study model as the first one created by the CART was able to extract useful hidden rules from a relatively small size dataset.

Analysis of aminoacids pattern in receptor tyrosine kinase using Boolean association rule.

PubMed

Kalita, Pranjal; Kumar, Brindha Senthil; Krishnaswamy, Soundararajan; Nachimuthu, Senthil Kumar

2012-01-01

Cancers are characterized by unrestricted cell division and independency of growth factor and other external signal responsiveness. Eukaryotic parental cells of tumors, on the other hand, constitute tissues and other higher structures like organs and systems and are capable of performing various functions in a highly co-ordinated fashion. Hence, cancer cells may be considered as entities capable of incessant growth and cell division but lacking any evolutionarily advanced intracellular or intercellular regulation. Since receptor tyrosine kinases are highly altered and exist in deregulated/constitutively active forms in cancer cells - achieved through various epigenetic mechanisms - we hypothesize the functional RTKs in cancer cells to resemble their counterparts in more primitive species. Analysis of RTK sequences of various species and of cancer is, therefore, expected to prove this hypothesis. Association rule in data mining can reveal the hidden biological information. This study utilizes the Boolean association rule to mine the occurrence pattern of glycine, arginine and alanine in receptor tyrosine kinases (RTKs) of invertebrates, vertebrates and cancer related vertebrate RTKs based on protein sequence informations. The results reveal that vertebrate cancer RTKs resembles prokaryotes and invertebrate RTKs showing an increasing trend of glycine, alanine and decreasing trend in arginine composition. The aminoacid compositions of vertebrates: invertebrates: prokaryotes: vertebrate cancer with respect to Glycine (>=6.1) were 42.86: 50.0: 85.71: 100%, Alanine (>=6.2) were 10.72: 66.67: 85.71: 100%, whereas Arginine (>=5.9) were 21.43: 16.67: 14.29: 0%, respectively. In conclusion, results from this study supports our hypothesis that cancer cells may resemble lower organisms since functionally cancer cells are unresponsive to external signals and various regulatory mechanisms typically found in higher eukaryotes are largely absent.

43 CFR 4.1272 - Interlocutory appeals.

Code of Federal Regulations, 2010 CFR

2010-10-01

... PROCEDURES Special Rules Applicable to Surface Coal Mining Hearings and Appeals Appeals to the Board from... modification of the administrative law judge's interlocutory ruling or order, the jurisdiction of the Board...

Prioritization of malaria endemic zones using self-organizing maps in the Manipur state of India.

PubMed

Murty, Upadhyayula Suryanarayana; Srinivasa Rao, Mutheneni; Misra, Sunil

2008-09-01

Due to the availability of a huge amount of epidemiological and public health data that require analysis and interpretation by using appropriate mathematical tools to support the existing method to control the mosquito and mosquito-borne diseases in a more effective way, data-mining tools are used to make sense from the chaos. Using data-mining tools, one can develop predictive models, patterns, association rules, and clusters of diseases, which can help the decision-makers in controlling the diseases. This paper mainly focuses on the applications of data-mining tools that have been used for the first time to prioritize the malaria endemic regions in Manipur state by using Self Organizing Maps (SOM). The SOM results (in two-dimensional images called Kohonen maps) clearly show the visual classification of malaria endemic zones into high, medium and low in the different districts of Manipur, and will be discussed in the paper.

A Recommendation Algorithm for Automating Corollary Order Generation

PubMed Central

Klann, Jeffrey; Schadow, Gunther; McCoy, JM

2009-01-01

Manual development and maintenance of decision support content is time-consuming and expensive. We explore recommendation algorithms, e-commerce data-mining tools that use collective order history to suggest purchases, to assist with this. In particular, previous work shows corollary order suggestions are amenable to automated data-mining techniques. Here, an item-based collaborative filtering algorithm augmented with association rule interestingness measures mined suggestions from 866,445 orders made in an inpatient hospital in 2007, generating 584 potential corollary orders. Our expert physician panel evaluated the top 92 and agreed 75.3% were clinically meaningful. Also, at least one felt 47.9% would be directly relevant in guideline development. This automated generation of a rough-cut of corollary orders confirms prior indications about automated tools in building decision support content. It is an important step toward computerized augmentation to decision support development, which could increase development efficiency and content quality while automatically capturing local standards. PMID:20351875

A recommendation algorithm for automating corollary order generation.

PubMed

Klann, Jeffrey; Schadow, Gunther; McCoy, J M

2009-11-14

Manual development and maintenance of decision support content is time-consuming and expensive. We explore recommendation algorithms, e-commerce data-mining tools that use collective order history to suggest purchases, to assist with this. In particular, previous work shows corollary order suggestions are amenable to automated data-mining techniques. Here, an item-based collaborative filtering algorithm augmented with association rule interestingness measures mined suggestions from 866,445 orders made in an inpatient hospital in 2007, generating 584 potential corollary orders. Our expert physician panel evaluated the top 92 and agreed 75.3% were clinically meaningful. Also, at least one felt 47.9% would be directly relevant in guideline development. This automated generation of a rough-cut of corollary orders confirms prior indications about automated tools in building decision support content. It is an important step toward computerized augmentation to decision support development, which could increase development efficiency and content quality while automatically capturing local standards.

Strawman II. Recommendations for a regulatory program for mining waste and materials under Subtitle D of the Resource Conservation and Recovery Act. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1990-05-21

The package, referred to as 'Strawman II', is a working document that represents EPA's latest staff position on an effective program to regulate wastes and other materials uniquely associated with noncoal mining. Strawman II does not represent a proposed rule. The package consists of two parts: (1) the Foreward, which describes the pre-rulemaking Strawman process, a background and overview of the mining waste program as envisioned in the package, and discussions of major issues concerning the program and its scope; and (2) the Regulatory Approach, presented as '40 CFR XXX, XXY, and XXZ' to reflect how the program might appearmore » in regulatory language. Discussions and amplifications of specific points are also interspersed throughout the Regulatory Approach. EPA encourages all interested parties to convey their views on any and all aspects of the program concept.« less

A case–control study of mesothelioma in Minnesota iron ore (taconite) miners

PubMed Central

Lambert, Christine S; Alexander, Bruce H; Ramachandran, Gurumurthy; MacLehose, Richard F; Nelson, Heather H; Ryan, Andrew D; Mandel, Jeffrey H

2018-01-01

Objectives An excess of mesothelioma has been observed in iron ore miners in Northeastern Minnesota. Mining and processing of taconite iron ore generate exposures that include elongate mineral particles (EMPs) of amphibole and non-amphibole origin. We conducted a nested case–control study of mesothelioma in a cohort of 68 737 iron ore miners (haematite and taconite ore miners) to evaluate the association between mesothelioma, employment and EMP exposures from taconite mining. Methods Mesothelioma cases (N=80) were identified through the Minnesota Cancer Surveillance System (MCSS) and death certificates. Four controls of similar age were selected for each case with 315 controls ultimately eligible for inclusion. Mesothelioma risk was evaluated by estimating rate ratios and 95% CIs with conditional logistic regression in relation to duration of taconite industry employment and cumulative EMP exposure [(EMP/cc)×years], defined by the National Institute for Occupational Safety and Health (NIOSH) 7400 method. Models were adjusted for employment in haematite mining and potential exposure to commercial asbestos products used in the industry. Results All mesothelioma cases were male and 57 of the cases had work experience in the taconite industry. Mesothelioma was associated with the number of years employed in the taconite industry (RR=1.03, 95% CI 1.00 to 1.06) and cumulative EMP exposure (RR=1.10, 95% CI 0.97 to –1.24). No association was observed with employment in haematite mining. Conclusions These results support an association between mesothelioma and employment duration and possibly EMP exposure in taconite mining and processing. The type of EMP was not determined. The potential role of commercial asbestos cannot be entirely ruled out. PMID:26655961

Finding novel relationships with integrated gene-gene association network analysis of Synechocystis sp. PCC 6803 using species-independent text-mining

PubMed Central

Kreula, Sanna M.; Kaewphan, Suwisa; Ginter, Filip

2018-01-01

The increasing move towards open access full-text scientific literature enhances our ability to utilize advanced text-mining methods to construct information-rich networks that no human will be able to grasp simply from ‘reading the literature’. The utility of text-mining for well-studied species is obvious though the utility for less studied species, or those with no prior track-record at all, is not clear. Here we present a concept for how advanced text-mining can be used to create information-rich networks even for less well studied species and apply it to generate an open-access gene-gene association network resource for Synechocystis sp. PCC 6803, a representative model organism for cyanobacteria and first case-study for the methodology. By merging the text-mining network with networks generated from species-specific experimental data, network integration was used to enhance the accuracy of predicting novel interactions that are biologically relevant. A rule-based algorithm (filter) was constructed in order to automate the search for novel candidate genes with a high degree of likely association to known target genes by (1) ignoring established relationships from the existing literature, as they are already ‘known’, and (2) demanding multiple independent evidences for every novel and potentially relevant relationship. Using selected case studies, we demonstrate the utility of the network resource and filter to (i) discover novel candidate associations between different genes or proteins in the network, and (ii) rapidly evaluate the potential role of any one particular gene or protein. The full network is provided as an open-source resource. PMID:29844966

A case-control study of mesothelioma in Minnesota iron ore (taconite) miners.

PubMed

Lambert, Christine S; Alexander, Bruce H; Ramachandran, Gurumurthy; MacLehose, Richard F; Nelson, Heather H; Ryan, Andrew D; Mandel, Jeffrey H

2016-02-01

An excess of mesothelioma has been observed in iron ore miners in Northeastern Minnesota. Mining and processing of taconite iron ore generate exposures that include elongate mineral particles (EMPs) of amphibole and non-amphibole origin. We conducted a nested case-control study of mesothelioma in a cohort of 68,737 iron ore miners (haematite and taconite ore miners) to evaluate the association between mesothelioma, employment and EMP exposures from taconite mining. Mesothelioma cases (N=80) were identified through the Minnesota Cancer Surveillance System (MCSS) and death certificates. Four controls of similar age were selected for each case with 315 controls ultimately eligible for inclusion. Mesothelioma risk was evaluated by estimating rate ratios and 95% CIs with conditional logistic regression in relation to duration of taconite industry employment and cumulative EMP exposure [(EMP/cc)×years], defined by the National Institute for Occupational Safety and Health (NIOSH) 7400 method. Models were adjusted for employment in haematite mining and potential exposure to commercial asbestos products used in the industry. All mesothelioma cases were male and 57 of the cases had work experience in the taconite industry. Mesothelioma was associated with the number of years employed in the taconite industry (RR=1.03, 95% CI 1.00 to 1.06) and cumulative EMP exposure (RR=1.10, 95% CI 0.97 to -1.24). No association was observed with employment in haematite mining. These results support an association between mesothelioma and employment duration and possibly EMP exposure in taconite mining and processing. The type of EMP was not determined. The potential role of commercial asbestos cannot be entirely ruled out. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

A New Framework for Textual Information Mining over Parse Trees. CRESST Report 805

ERIC Educational Resources Information Center

Mousavi, Hamid; Kerr, Deirdre; Iseli, Markus R.

2011-01-01

Textual information mining is a challenging problem that has resulted in the creation of many different rule-based linguistic query languages. However, these languages generally are not optimized for the purpose of text mining. In other words, they usually consider queries as individuals and only return raw results for each query. Moreover they…

Documents for SBAR Panel: CERCLA 108(b) Hard Rock Mining Financial Assurance Rule

EPA Pesticide Factsheets

SBAR panel documents for small business advocacy review panel on the financial responsibilities of the hard rock mining industry under Section 108(b) of the Comprehensive Environmental Response, Compensation, and Liability Act

Preference Mining Using Neighborhood Rough Set Model on Two Universes.

PubMed

Zeng, Kai

2016-01-01

Preference mining plays an important role in e-commerce and video websites for enhancing user satisfaction and loyalty. Some classical methods are not available for the cold-start problem when the user or the item is new. In this paper, we propose a new model, called parametric neighborhood rough set on two universes (NRSTU), to describe the user and item data structures. Furthermore, the neighborhood lower approximation operator is used for defining the preference rules. Then, we provide the means for recommending items to users by using these rules. Finally, we give an experimental example to show the details of NRSTU-based preference mining for cold-start problem. The parameters of the model are also discussed. The experimental results show that the proposed method presents an effective solution for preference mining. In particular, NRSTU improves the recommendation accuracy by about 19% compared to the traditional method.

LimTox: a web tool for applied text mining of adverse event and toxicity associations of compounds, drugs and genes

PubMed Central

Cañada, Andres; Rabal, Obdulia; Oyarzabal, Julen; Valencia, Alfonso

2017-01-01

Abstract A considerable effort has been devoted to retrieve systematically information for genes and proteins as well as relationships between them. Despite the importance of chemical compounds and drugs as a central bio-entity in pharmacological and biological research, only a limited number of freely available chemical text-mining/search engine technologies are currently accessible. Here we present LimTox (Literature Mining for Toxicology), a web-based online biomedical search tool with special focus on adverse hepatobiliary reactions. It integrates a range of text mining, named entity recognition and information extraction components. LimTox relies on machine-learning, rule-based, pattern-based and term lookup strategies. This system processes scientific abstracts, a set of full text articles and medical agency assessment reports. Although the main focus of LimTox is on adverse liver events, it enables also basic searches for other organ level toxicity associations (nephrotoxicity, cardiotoxicity, thyrotoxicity and phospholipidosis). This tool supports specialized search queries for: chemical compounds/drugs, genes (with additional emphasis on key enzymes in drug metabolism, namely P450 cytochromes—CYPs) and biochemical liver markers. The LimTox website is free and open to all users and there is no login requirement. LimTox can be accessed at: http://limtox.bioinfo.cnio.es PMID:28531339

Mining the human genome after Association for Molecular Pathology v. Myriad Genetics

PubMed Central

Evans, Barbara J

2014-01-01

The Supreme Court's recent decision in Association for Molecular Pathology v. Myriad Genetics portrays the human genome as a product of nature. This frames medical genetics as an extractive industry that mines a natural resource to produce valuable goods and services. Natural resource law offers insights into problems medical geneticists can expect after this decision and suggests possible solutions. Increased competition among clinical laboratories offers various benefits but threatens to increase fragmentation of genetic data resources, potentially causing waste in the form of lost opportunities to discover the clinical significance of particular gene variants. The solution lies in addressing legal barriers to appropriate data sharing. Sustainable discovery in the field of medical genetics can best be achieved through voluntary data sharing rather than command-and-control tactics, but voluntary mechanisms must be conceived broadly to include market-based approaches as well as donative and publicly funded data commons. The recently revised Health Insurance Portability and Accountability Act Privacy Rule offers an improved—but still imperfect—framework for market-oriented data sharing. This article explores strategies for addressing the Privacy Rule's remaining defects. America is close to having a legal framework that can reward innovators, protect privacy, and promote needed data sharing to advance medical genetics. Genet Med 16 7, 504–509. PMID:24357850

Mining the human genome after Association for Molecular Pathology v. Myriad Genetics.

PubMed

Evans, Barbara J

2014-07-01

The Supreme Court's recent decision in Association for Molecular Pathology v. Myriad Genetics portrays the human genome as a product of nature. This frames medical genetics as an extractive industry that mines a natural resource to produce valuable goods and services. Natural resource law offers insights into problems medical geneticists can expect after this decision and suggests possible solutions. Increased competition among clinical laboratories offers various benefits but threatens to increase fragmentation of genetic data resources, potentially causing waste in the form of lost opportunities to discover the clinical significance of particular gene variants. The solution lies in addressing legal barriers to appropriate data sharing. Sustainable discovery in the field of medical genetics can best be achieved through voluntary data sharing rather than command-and-control tactics, but voluntary mechanisms must be conceived broadly to include market-based approaches as well as donative and publicly funded data commons. The recently revised Health Insurance Portability and Accountability Act Privacy Rule offers an improved--but still imperfect--framework for market-oriented data sharing. This article explores strategies for addressing the Privacy Rule's remaining defects. America is close to having a legal framework that can reward innovators, protect privacy, and promote needed data sharing to advance medical genetics.

CARSVM: a class association rule-based classification framework and its application to gene expression data.

PubMed

Kianmehr, Keivan; Alhajj, Reda

2008-09-01

In this study, we aim at building a classification framework, namely the CARSVM model, which integrates association rule mining and support vector machine (SVM). The goal is to benefit from advantages of both, the discriminative knowledge represented by class association rules and the classification power of the SVM algorithm, to construct an efficient and accurate classifier model that improves the interpretability problem of SVM as a traditional machine learning technique and overcomes the efficiency issues of associative classification algorithms. In our proposed framework: instead of using the original training set, a set of rule-based feature vectors, which are generated based on the discriminative ability of class association rules over the training samples, are presented to the learning component of the SVM algorithm. We show that rule-based feature vectors present a high-qualified source of discrimination knowledge that can impact substantially the prediction power of SVM and associative classification techniques. They provide users with more conveniences in terms of understandability and interpretability as well. We have used four datasets from UCI ML repository to evaluate the performance of the developed system in comparison with five well-known existing classification methods. Because of the importance and popularity of gene expression analysis as real world application of the classification model, we present an extension of CARSVM combined with feature selection to be applied to gene expression data. Then, we describe how this combination will provide biologists with an efficient and understandable classifier model. The reported test results and their biological interpretation demonstrate the applicability, efficiency and effectiveness of the proposed model. From the results, it can be concluded that a considerable increase in classification accuracy can be obtained when the rule-based feature vectors are integrated in the learning process of the SVM algorithm. In the context of applicability, according to the results obtained from gene expression analysis, we can conclude that the CARSVM system can be utilized in a variety of real world applications with some adjustments.

Study of the factors associated with substance use in adolescence using Association Rules.

PubMed

García, Elena Gervilla; Blasco, Berta Cajal; López, Rafael Jiménez; Pol, Alfonso Palmer

2010-01-01

The aim of this study is to analyse the factors related to the use of addictive substances in adolescence using association rules, descriptive tools included in Data Mining. Thus, we have a database referring to the consumption of addictive substances in adolescence, and use the free distribution program in the R arules package (version 2.10.0). The sample was made up of 9,300 students between the ages of 14 and 18 (47.1% boys and 52.9% girls) with an average age of 15.6 (SE=1.2). The adolescents answered an anonymous questionnaire on personal, family and environmental risk factors related to substance use. The best rules obtained with regard to substance use relate the consumption of alcohol to perceived parenting style and peer consumption (confidence = 0.8528), the use of tobacco (smoking), cannabis and cocaine to perceived parental action and illegal behaviour (confidence = 0.8032, 0.8718 and 1.0000, respectively), and the use of ecstasy to peer consumption (confidence = 1.0000). In general, the association rules show in a simple manner the relationship between certain patterns of perceived parental action, behaviours that deviate from social behavioural norms, peer consumption and the use of different legal and illegal drugs of abuse in adolescence. The implications of the results obtained are described, together with the usefulness of this new methodology of analysis.

[Exploring pharmacological principle of Artemisia carvifolia with textmining technology].

PubMed

Zhao, Yu-Ping; Wang, Hui; Yang, Guang; Qiu, Zhi-Dong; Qu, Xiao-Bo; Zhang, Xiao-Bo

2016-08-01

To explore the pharmacological principle of Artemisia carvifolia,the text mining technique was used. All the references of A. carvifolia were collected from PubMed database, and then the rules of the main ingredient,relative diseases, organs, tissues, proteins and metabolites were analyzed. Finally, a network was set up. Then it was found that the main ingredients included sesquiterpenoids,flavonoids,and volatileoils.The diseases such as malaria, cerebral malaria, falciparum malaria, visceral leishmaniasis and systemic lupus erythematosus were often treated with A. carvifolia. In association in organ were the liver, skin, trachea,lungs,and spleen.Correlations with tissues were mainly including macrophages, T lymphocytes, blood vessels, epithelial cells.The protein was correlation with it involved CYP450, PI3K, TNF-α, AASDPPT, DNA polymerase and so on. Comprehensive and systematic treatment principle of A. carvifolia was obtained by text mining, which was helpful in clinical application. Copyright© by the Chinese Pharmaceutical Association.

Efficient Mining and Detection of Sequential Intrusion Patterns for Network Intrusion Detection Systems

NASA Astrophysics Data System (ADS)

Shyu, Mei-Ling; Huang, Zifang; Luo, Hongli

In recent years, pervasive computing infrastructures have greatly improved the interaction between human and system. As we put more reliance on these computing infrastructures, we also face threats of network intrusion and/or any new forms of undesirable IT-based activities. Hence, network security has become an extremely important issue, which is closely connected with homeland security, business transactions, and people's daily life. Accurate and efficient intrusion detection technologies are required to safeguard the network systems and the critical information transmitted in the network systems. In this chapter, a novel network intrusion detection framework for mining and detecting sequential intrusion patterns is proposed. The proposed framework consists of a Collateral Representative Subspace Projection Modeling (C-RSPM) component for supervised classification, and an inter-transactional association rule mining method based on Layer Divided Modeling (LDM) for temporal pattern analysis. Experiments on the KDD99 data set and the traffic data set generated by a private LAN testbed show promising results with high detection rates, low processing time, and low false alarm rates in mining and detecting sequential intrusion detections.

MinE conformational dynamics regulate membrane binding, MinD interaction, and Min oscillation

PubMed Central

Park, Kyung-Tae; Villar, Maria T.; Artigues, Antonio; Lutkenhaus, Joe

2017-01-01

In Escherichia coli MinE induces MinC/MinD to oscillate between the ends of the cell, contributing to the precise placement of the Z ring at midcell. To do this, MinE undergoes a remarkable conformational change from a latent 6β-stranded form that diffuses in the cytoplasm to an active 4β-stranded form bound to the membrane and MinD. How this conformational switch occurs is not known. Here, using hydrogen–deuterium exchange coupled to mass spectrometry (HDX-MS) we rule out a model in which the two forms are in rapid equilibrium. Furthermore, HDX-MS revealed that a MinE mutant (D45A/V49A), previously shown to produce an aberrant oscillation and unable to assemble a MinE ring, is more rigid than WT MinE. This mutant has a defect in interaction with MinD, suggesting it has difficulty in switching to the active form. Analysis of intragenic suppressors of this mutant suggests it has difficulty in releasing the N-terminal membrane targeting sequences (MTS). These results indicate that the dynamic association of the MTS with the β-sheet is fine-tuned to balance MinE’s need to sense MinD on the membrane with its need to diffuse in the cytoplasm, both of which are necessary for the oscillation. The results lead to models for MinE activation and MinE ring formation. PMID:28652337

Information pricing based on trusted system

NASA Astrophysics Data System (ADS)

Liu, Zehua; Zhang, Nan; Han, Hongfeng

2018-05-01

Personal information has become a valuable commodity in today's society. So our goal aims to develop a price point and a pricing system to be realistic. First of all, we improve the existing BLP system to prevent cascading incidents, design a 7-layer model. Through the cost of encryption in each layer, we develop PI price points. Besides, we use association rules mining algorithms in data mining algorithms to calculate the importance of information in order to optimize informational hierarchies of different attribute types when located within a multi-level trusted system. Finally, we use normal distribution model to predict encryption level distribution for users in different classes and then calculate information prices through a linear programming model with the help of encryption level distribution above.

20 CFR 410.681 - Change of ruling or legal precedent.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 20 Employees' Benefits 2 2011-04-01 2011-04-01 false Change of ruling or legal precedent. 410.681 Section 410.681 Employees' Benefits SOCIAL SECURITY ADMINISTRATION FEDERAL COAL MINE HEALTH AND SAFETY ACT..., Administrative Review, Finality of Decisions, and Representation of Parties § 410.681 Change of ruling or legal...

20 CFR 410.681 - Change of ruling or legal precedent.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 20 Employees' Benefits 2 2010-04-01 2010-04-01 false Change of ruling or legal precedent. 410.681 Section 410.681 Employees' Benefits SOCIAL SECURITY ADMINISTRATION FEDERAL COAL MINE HEALTH AND SAFETY ACT..., Administrative Review, Finality of Decisions, and Representation of Parties § 410.681 Change of ruling or legal...

Japanese Aggression in Asia (1895-1930). Japan’s Dream of ’Hakko Ichuo’ (Eight Corners of the World under Japanese Rule).

DTIC Science & Technology

1980-12-01

the British Navy was also of significant value, for then Britannia still ruled the waves. The huge indemnity received from the Chinese played an...11 among the sons, the eldest took all and the second and third sons became either factory or mine workers or apprentices of a merchant. When...warehouses, spin- ning, paper and sugar mills, all based on the large profits which came from banking, mining and foreign trade. Mitsubishi had its

British Defense Policy: A New Approach?

DTIC Science & Technology

1988-12-14

inherent to their well-being, was also acknowledged by the remainder of the world in its attitude toward Britain. Is not "Rule Britannia , Britannia ...Castle Class 1 1 Island Class 7 43 Mine -Counter Minesweepers 2 2 Mine River Class 12 Ton Class 10 3 Hunt Class 12 1 Patrol Craft Bird Class 5 Coastal 15...submarine warfare carriers, assault ships, and mine -counter mine vessels. British naval aircraft is as depicted in Table 2. Table 2. Aircraft of the Royal

77 FR 54490 - Alabama Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2012-09-05

... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and opportunity for public hearing on proposed amendment. SUMMARY: We, the Office of Surface Mining Reclamation... will follow for the public hearing, if one is requested. DATES: We will accept written comments on this...

Characteristics of cyclist crashes in Italy using latent class analysis and association rule mining

PubMed Central

De Angelis, Marco; Marín Puchades, Víctor; Fraboni, Federico; Pietrantoni, Luca

2017-01-01

The factors associated with severity of the bicycle crashes may differ across different bicycle crash patterns. Therefore, it is important to identify distinct bicycle crash patterns with homogeneous attributes. The current study aimed at identifying subgroups of bicycle crashes in Italy and analyzing separately the different bicycle crash types. The present study focused on bicycle crashes that occurred in Italy during the period between 2011 and 2013. We analyzed categorical indicators corresponding to the characteristics of infrastructure (road type, road signage, and location type), road user (i.e., opponent vehicle and cyclist’s maneuver, type of collision, age and gender of the cyclist), vehicle (type of opponent vehicle), and the environmental and time period variables (time of the day, day of the week, season, pavement condition, and weather). To identify homogenous subgroups of bicycle crashes, we used latent class analysis. Using latent class analysis, the bicycle crash data set was segmented into 19 classes, which represents 19 different bicycle crash types. Logistic regression analysis was used to identify the association between class membership and severity of the bicycle crashes. Finally, association rules were conducted for each of the latent classes to uncover the factors associated with an increased likelihood of severity. Association rules highlighted different crash characteristics associated with an increased likelihood of severity for each of the 19 bicycle crash types. PMID:28158296

A novel artificial immune clonal selection classification and rule mining with swarm learning model

NASA Astrophysics Data System (ADS)

Al-Sheshtawi, Khaled A.; Abdul-Kader, Hatem M.; Elsisi, Ashraf B.

2013-06-01

Metaheuristic optimisation algorithms have become popular choice for solving complex problems. By integrating Artificial Immune clonal selection algorithm (CSA) and particle swarm optimisation (PSO) algorithm, a novel hybrid Clonal Selection Classification and Rule Mining with Swarm Learning Algorithm (CS2) is proposed. The main goal of the approach is to exploit and explore the parallel computation merit of Clonal Selection and the speed and self-organisation merits of Particle Swarm by sharing information between clonal selection population and particle swarm. Hence, we employed the advantages of PSO to improve the mutation mechanism of the artificial immune CSA and to mine classification rules within datasets. Consequently, our proposed algorithm required less training time and memory cells in comparison to other AIS algorithms. In this paper, classification rule mining has been modelled as a miltiobjective optimisation problem with predictive accuracy. The multiobjective approach is intended to allow the PSO algorithm to return an approximation to the accuracy and comprehensibility border, containing solutions that are spread across the border. We compared our proposed algorithm classification accuracy CS2 with five commonly used CSAs, namely: AIRS1, AIRS2, AIRS-Parallel, CLONALG, and CSCA using eight benchmark datasets. We also compared our proposed algorithm classification accuracy CS2 with other five methods, namely: Naïve Bayes, SVM, MLP, CART, and RFB. The results show that the proposed algorithm is comparable to the 10 studied algorithms. As a result, the hybridisation, built of CSA and PSO, can develop respective merit, compensate opponent defect, and make search-optimal effect and speed better.

Urinary metabolic profiling of asymptomatic acute intermittent porphyria using a rule-mining-based algorithm.

PubMed

Luck, Margaux; Schmitt, Caroline; Talbi, Neila; Gouya, Laurent; Caradeuc, Cédric; Puy, Hervé; Bertho, Gildas; Pallet, Nicolas

2018-01-01

Metabolomic profiling combines Nuclear Magnetic Resonance spectroscopy with supervised statistical analysis that might allow to better understanding the mechanisms of a disease. In this study, the urinary metabolic profiling of individuals with porphyrias was performed to predict different types of disease, and to propose new pathophysiological hypotheses. Urine 1 H-NMR spectra of 73 patients with asymptomatic acute intermittent porphyria (aAIP) and familial or sporadic porphyria cutanea tarda (f/sPCT) were compared using a supervised rule-mining algorithm. NMR spectrum buckets bins, corresponding to rules, were extracted and a logistic regression was trained. Our rule-mining algorithm generated results were consistent with those obtained using partial least square discriminant analysis (PLS-DA) and the predictive performance of the model was significant. Buckets that were identified by the algorithm corresponded to metabolites involved in glycolysis and energy-conversion pathways, notably acetate, citrate, and pyruvate, which were found in higher concentrations in the urines of aAIP compared with PCT patients. Metabolic profiling did not discriminate sPCT from fPCT patients. These results suggest that metabolic reprogramming occurs in aAIP individuals, even in the absence of overt symptoms, and supports the relationship that occur between heme synthesis and mitochondrial energetic metabolism.

26 CFR 1.614-3 - Rules relating to separate operating mineral interests in the case of mines.

Code of Federal Regulations, 2010 CFR

2010-04-01

... method of mining the mineral, the location of the excavations or other workings in relation to the mineral deposit or deposits, and the topography of the area. The determination of the taxpayer as to the...

30 CFR 56.18006 - New employees.

Code of Federal Regulations, 2010 CFR

2010-07-01

... New employees. New employees shall be indoctrinated in safety rules and safe work procedures. ... 30 Mineral Resources 1 2010-07-01 2010-07-01 false New employees. 56.18006 Section 56.18006 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL MINE...

The adaptive approach for storage assignment by mining data of warehouse management system for distribution centres

NASA Astrophysics Data System (ADS)

Ming-Huang Chiang, David; Lin, Chia-Ping; Chen, Mu-Chen

2011-05-01

Among distribution centre operations, order picking has been reported to be the most labour-intensive activity. Sophisticated storage assignment policies adopted to reduce the travel distance of order picking have been explored in the literature. Unfortunately, previous research has been devoted to locating entire products from scratch. Instead, this study intends to propose an adaptive approach, a Data Mining-based Storage Assignment approach (DMSA), to find the optimal storage assignment for newly delivered products that need to be put away when there is vacant shelf space in a distribution centre. In the DMSA, a new association index (AIX) is developed to evaluate the fitness between the put away products and the unassigned storage locations by applying association rule mining. With AIX, the storage location assignment problem (SLAP) can be formulated and solved as a binary integer programming. To evaluate the performance of DMSA, a real-world order database of a distribution centre is obtained and used to compare the results from DMSA with a random assignment approach. It turns out that DMSA outperforms random assignment as the number of put away products and the proportion of put away products with high turnover rates increase.

LimTox: a web tool for applied text mining of adverse event and toxicity associations of compounds, drugs and genes.

PubMed

Cañada, Andres; Capella-Gutierrez, Salvador; Rabal, Obdulia; Oyarzabal, Julen; Valencia, Alfonso; Krallinger, Martin

2017-07-03

A considerable effort has been devoted to retrieve systematically information for genes and proteins as well as relationships between them. Despite the importance of chemical compounds and drugs as a central bio-entity in pharmacological and biological research, only a limited number of freely available chemical text-mining/search engine technologies are currently accessible. Here we present LimTox (Literature Mining for Toxicology), a web-based online biomedical search tool with special focus on adverse hepatobiliary reactions. It integrates a range of text mining, named entity recognition and information extraction components. LimTox relies on machine-learning, rule-based, pattern-based and term lookup strategies. This system processes scientific abstracts, a set of full text articles and medical agency assessment reports. Although the main focus of LimTox is on adverse liver events, it enables also basic searches for other organ level toxicity associations (nephrotoxicity, cardiotoxicity, thyrotoxicity and phospholipidosis). This tool supports specialized search queries for: chemical compounds/drugs, genes (with additional emphasis on key enzymes in drug metabolism, namely P450 cytochromes-CYPs) and biochemical liver markers. The LimTox website is free and open to all users and there is no login requirement. LimTox can be accessed at: http://limtox.bioinfo.cnio.es. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Educational Data Mining Application for Estimating Students Performance in Weka Environment

NASA Astrophysics Data System (ADS)

Gowri, G. Shiyamala; Thulasiram, Ramasamy; Amit Baburao, Mahindra

2017-11-01

Educational data mining (EDM) is a multi-disciplinary research area that examines artificial intelligence, statistical modeling and data mining with the data generated from an educational institution. EDM utilizes computational ways to deal with explicate educational information keeping in mind the end goal to examine educational inquiries. To make a country stand unique among the other nations of the world, the education system has to undergo a major transition by redesigning its framework. The concealed patterns and data from various information repositories can be extracted by adopting the techniques of data mining. In order to summarize the performance of students with their credentials, we scrutinize the exploitation of data mining in the field of academics. Apriori algorithmic procedure is extensively applied to the database of students for a wider classification based on various categorizes. K-means procedure is applied to the same set of databases in order to accumulate them into a specific category. Apriori algorithm deals with mining the rules in order to extract patterns that are similar along with their associations in relation to various set of records. The records can be extracted from academic information repositories. The parameters used in this study gives more importance to psychological traits than academic features. The undesirable student conduct can be clearly witnessed if we make use of information mining frameworks. Thus, the algorithms efficiently prove to profile the students in any educational environment. The ultimate objective of the study is to suspect if a student is prone to violence or not.

20 CFR 410.703 - Adjudicatory rules for determining entitlement to benefits.

Code of Federal Regulations, 2010 CFR

2010-04-01

... COAL MINE HEALTH AND SAFETY ACT OF 1969, TITLE IV-BLACK LUNG BENEFITS (1969- ) Rules for the Review of Denied and Pending Claims Under the Black Lung Benefits Reform Act (BLBRA) of 1977 § 410.703 Adjudicatory...

20 CFR 410.703 - Adjudicatory rules for determining entitlement to benefits.

Code of Federal Regulations, 2011 CFR

2011-04-01

... COAL MINE HEALTH AND SAFETY ACT OF 1969, TITLE IV-BLACK LUNG BENEFITS (1969- ) Rules for the Review of Denied and Pending Claims Under the Black Lung Benefits Reform Act (BLBRA) of 1977 § 410.703 Adjudicatory...

Hedyotis diffusa Combined with Scutellaria barbata Are the Core Treatment of Chinese Herbal Medicine Used for Breast Cancer Patients: A Population-Based Study

PubMed Central

Yeh, Yuan-Chieh; Chen, Hsing-Yu; Yang, Sien-Hung; Lin, Yi-Hsien; Chiu, Jen-Hwey; Lin, Yi-Hsuan; Chen, Jiun-Liang

2014-01-01

Traditional Chinese medicine (TCM), which is the most common type of complementary and alternative medicine (CAM) used in Taiwan, is increasingly used to treat patients with breast cancer. However, large-scale studies on the patterns of TCM prescriptions for breast cancer are still lacking. The aim of this study was to determine the core treatment of TCM prescriptions used for breast cancer recorded in the Taiwan National Health Insurance Research Database. TCM visits made for breast cancer in 2008 were identified using ICD-9 codes. The prescriptions obtained at these TCM visits were evaluated using association rule mining to evaluate the combinations of Chinese herbal medicine (CHM) used to treat breast cancer patients. A total of 37,176 prescriptions were made for 4,436 outpatients with breast cancer. Association rule mining and network analysis identified Hedyotis diffusa plus Scutellaria barbata as the most common duplex medicinal (10.9%) used for the core treatment of breast cancer. Jia-Wei-Xiao-Yao-San (19.6%) and Hedyotis diffusa (41.9%) were the most commonly prescribed herbal formula (HF) and single herb (SH), respectively. Only 35% of the commonly used CHM had been studied for efficacy. More clinical trials are needed to evaluate the efficacy and safety of these CHM used to treat breast cancer. PMID:24734104

Hedyotis diffusa Combined with Scutellaria barbata Are the Core Treatment of Chinese Herbal Medicine Used for Breast Cancer Patients: A Population-Based Study.

PubMed

Yeh, Yuan-Chieh; Chen, Hsing-Yu; Yang, Sien-Hung; Lin, Yi-Hsien; Chiu, Jen-Hwey; Lin, Yi-Hsuan; Chen, Jiun-Liang

2014-01-01

Traditional Chinese medicine (TCM), which is the most common type of complementary and alternative medicine (CAM) used in Taiwan, is increasingly used to treat patients with breast cancer. However, large-scale studies on the patterns of TCM prescriptions for breast cancer are still lacking. The aim of this study was to determine the core treatment of TCM prescriptions used for breast cancer recorded in the Taiwan National Health Insurance Research Database. TCM visits made for breast cancer in 2008 were identified using ICD-9 codes. The prescriptions obtained at these TCM visits were evaluated using association rule mining to evaluate the combinations of Chinese herbal medicine (CHM) used to treat breast cancer patients. A total of 37,176 prescriptions were made for 4,436 outpatients with breast cancer. Association rule mining and network analysis identified Hedyotis diffusa plus Scutellaria barbata as the most common duplex medicinal (10.9%) used for the core treatment of breast cancer. Jia-Wei-Xiao-Yao-San (19.6%) and Hedyotis diffusa (41.9%) were the most commonly prescribed herbal formula (HF) and single herb (SH), respectively. Only 35% of the commonly used CHM had been studied for efficacy. More clinical trials are needed to evaluate the efficacy and safety of these CHM used to treat breast cancer.

43 CFR 4.1383 - Hearing.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 43 Public Lands: Interior 1 2014-10-01 2014-10-01 false Hearing. 4.1383 Section 4.1383 Public Lands: Interior Office of the Secretary of the Interior DEPARTMENT HEARINGS AND APPEALS PROCEDURES Special Rules Applicable to Surface Coal Mining Hearings and Appeals Review of Office of Surface Mining...

30 CFR 48.6 - Experienced miner training.

Code of Federal Regulations, 2010 CFR

2010-07-01

.... (b) Experienced miners must complete the training prescribed in this section before beginning work... to work environment. The course shall include a visit and tour of the mine. The methods of mining... responsibilities of such supervisors and miners' representatives; and an introduction to the operator's rules and...

43 CFR 3483.6 - Special logical mining unit rules.

Code of Federal Regulations, 2011 CFR

2011-10-01

... the LMU, of either Federal or non-Federal recoverable coal reserves or a combination thereof, shall be... Section 3483.6 Public Lands: Interior Regulations Relating to Public Lands (Continued) BUREAU OF LAND MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) COAL EXPLORATION AND MINING OPERATIONS...

30 CFR 939.700 - Rhode Island Federal program.

Code of Federal Regulations, 2013 CFR

2013-07-01

... Rhode Island Federal program. (a) This part contains all rules that are applicable to surface coal... to all surface coal mining and reclamation operations in Rhode Island conducted on non-Federal and... stringent environmental control and regulation of surface coal mining and reclamation operations than do the...

43 CFR 3483.6 - Special logical mining unit rules.

Code of Federal Regulations, 2013 CFR

2013-10-01

... the LMU, of either Federal or non-Federal recoverable coal reserves or a combination thereof, shall be... Section 3483.6 Public Lands: Interior Regulations Relating to Public Lands (Continued) BUREAU OF LAND MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) COAL EXPLORATION AND MINING OPERATIONS...

43 CFR 3483.6 - Special logical mining unit rules.

Code of Federal Regulations, 2014 CFR

2014-10-01

... the LMU, of either Federal or non-Federal recoverable coal reserves or a combination thereof, shall be... Section 3483.6 Public Lands: Interior Regulations Relating to Public Lands (Continued) BUREAU OF LAND MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) COAL EXPLORATION AND MINING OPERATIONS...

43 CFR 4.1351 - Preliminary finding by OSM.

Code of Federal Regulations, 2010 CFR

2010-10-01

... APPEALS PROCEDURES Special Rules Applicable to Surface Coal Mining Hearings and Appeals Request for...(c) of the Act, 30 U.s.c. 1260(c) (federal Program; Federal Lands Program; Federal Program for Indian... or has controlled surface coal mining and reclamation operations with a demonstrated pattern of...

43 CFR 3483.6 - Special logical mining unit rules.

Code of Federal Regulations, 2012 CFR

2012-10-01

... the LMU, of either Federal or non-Federal recoverable coal reserves or a combination thereof, shall be... Section 3483.6 Public Lands: Interior Regulations Relating to Public Lands (Continued) BUREAU OF LAND MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) COAL EXPLORATION AND MINING OPERATIONS...

43 CFR 4.1383 - Hearing.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 43 Public Lands: Interior 1 2010-10-01 2010-10-01 false Hearing. 4.1383 Section 4.1383 Public Lands: Interior Office of the Secretary of the Interior DEPARTMENT HEARINGS AND APPEALS PROCEDURES Special Rules Applicable to Surface Coal Mining Hearings and Appeals Review of Office of Surface Mining...

Exploring Characterizations of Learning Object Repositories Using Data Mining Techniques

NASA Astrophysics Data System (ADS)

Segura, Alejandra; Vidal, Christian; Menendez, Victor; Zapata, Alfredo; Prieto, Manuel

Learning object repositories provide a platform for the sharing of Web-based educational resources. As these repositories evolve independently, it is difficult for users to have a clear picture of the kind of contents they give access to. Metadata can be used to automatically extract a characterization of these resources by using machine learning techniques. This paper presents an exploratory study carried out in the contents of four public repositories that uses clustering and association rule mining algorithms to extract characterizations of repository contents. The results of the analysis include potential relationships between different attributes of learning objects that may be useful to gain an understanding of the kind of resources available and eventually develop search mechanisms that consider repository descriptions as a criteria in federated search.

75 FR 26828 - Self-Regulatory Organizations; Chicago Board Options Exchange, Incorporated; Notice of Filing and...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-05-12

... amend [sic] its rules relating to the Penny Pilot Program. The text of the rule proposal is available on... proposed rule change. The text of those statements may be examined at the places specified in Item IV below... Technology Select Sector XME SPDR S&P Metals & Mining SPDR Fund. ETF. AKS AK Steel Holding Corp... KGC...

Federal Register Notice for the Mining Waste Exclusion Final Rule, September 1, 1989

EPA Pesticide Factsheets

Final rule responding to a federal Appeals Court directive to narrow the exclusion of solid waste from the extraction, beneficiation, and processing of ores and minerals from regulation as hazardous waste as it applies to mineral processing wastes.

43 CFR 4.1109 - Service.

Code of Federal Regulations, 2010 CFR

2010-10-01

... Special Rules Applicable to Surface Coal Mining Hearings and Appeals General Provisions § 4.1109 Service.... Department of the Interior, representing OSMRE in the state in which the mining operation at issue is located, and on any other statutory parties specified under § 4.1105 of this part. (2) The jurisdictions...

78 FR 37404 - Small Business Size Standards: Support Activities for Mining

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-20

... SMALL BUSINESS ADMINISTRATION 13 CFR Part 121 RIN 3245-AG44 Small Business Size Standards: Support Activities for Mining AGENCY: U.S. Small Business Administration. ACTION: Final rule. SUMMARY: The United States Small Business Administration (SBA) is increasing the small business size standards for three of...

26 CFR 1.611-5 - Depreciation of improvements.

Code of Federal Regulations, 2011 CFR

2011-04-01

... (CONTINUED) INCOME TAXES (CONTINUED) Natural Resources § 1.611-5 Depreciation of improvements. (a) In general. Section 611 provides in the case of mines, oil and gas wells, other natural deposits, and timber that...). (b) Special rules for mines, oil and gas wells, other natural deposits and timber. (1) For principles...

75 FR 21987 - Penalty Settlement Procedure

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-27

... and Health Act of 1977, or Mine Act. Hearings are held before the Commission's Administrative Law... settling civil penalties assessed under the Mine Act. DATES: The interim rule takes effect on May 27, 2010... Commission has explored is to simplify how it processes civil penalty settlements. Under section 110(k) of...

30 CFR 906.1 - Scope.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 3 2010-07-01 2010-07-01 false Scope. 906.1 Section 906.1 Mineral Resources... OF SURFACE MINING OPERATIONS WITHIN EACH STATE COLORADO § 906.1 Scope. This part contains all rules applicable only within Colorado that have been adopted under the Surface Mining Control and Reclamation Act...

75 FR 52980 - Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-30

.../maintaining): $303,512. Description: The Safety Standards for Underground Coal Mine Ventilation Belt Entry rule provides safety requirements for the use of the conveyor belt entry as a ventilation intake to... Underground Coal Mine Ventilation--Belt Entry Used as an Intake Air Course to Ventilate Working Sections and...

Data Mining in Health and Medical Information.

ERIC Educational Resources Information Center

Bath, Peter A.

2004-01-01

Presents a literature review that covers the following topics related to data mining (DM) in health and medical information: the potential of DM in health and medicine; statistical methods; evaluation of methods; DM tools for health and medicine; inductive learning of symbolic rules; application of DM tools in diagnosis and prognosis; and…

Army Needs to Identify Government Purchase Card High-Risk Transactions

DTIC Science & Technology

2012-01-20

Purchase Card Program Data Mining Process Needs Improvement 11...Mining Process Needs Improvement The 17 transactions that were noncompliant occurred because cardholders ignored the GPC business rules so the...Scope and Methodology 16 Use of Computer- Processed Data 16 Use of Technical Assistance 17 Prior Coverage

A Study of Pattern Prediction in the Monitoring Data of Earthen Ruins with the Internet of Things.

PubMed

Xiao, Yun; Wang, Xin; Eshragh, Faezeh; Wang, Xuanhong; Chen, Xiaojiang; Fang, Dingyi

2017-05-11

An understanding of the changes of the rammed earth temperature of earthen ruins is important for protection of such ruins. To predict the rammed earth temperature pattern using the air temperature pattern of the monitoring data of earthen ruins, a pattern prediction method based on interesting pattern mining and correlation, called PPER, is proposed in this paper. PPER first finds the interesting patterns in the air temperature sequence and the rammed earth temperature sequence. To reduce the processing time, two pruning rules and a new data structure based on an R-tree are also proposed. Correlation rules between the air temperature patterns and the rammed earth temperature patterns are then mined. The correlation rules are merged into predictive rules for the rammed earth temperature pattern. Experiments were conducted to show the accuracy of the presented method and the power of the pruning rules. Moreover, the Ming Dynasty Great Wall dataset was used to examine the algorithm, and six predictive rules from the air temperature to rammed earth temperature based on the interesting patterns were obtained, with the average hit rate reaching 89.8%. The PPER and predictive rules will be useful for rammed earth temperature prediction in protection of earthen ruins.

Detecting Malicious Tweets in Twitter Using Runtime Monitoring With Hidden Information

DTIC Science & Technology

2016-06-01

text mining using Twitter streaming API and python [Online]. Available: http://adilmoujahid.com/posts/2014/07/twitter-analytics/ [22] M. Singh, B...sites with 645,750,000 registered users [3] and has open source public tweets for data mining . 2. Malicious Users and Tweets In the modern world...want to data mine in Twitter, and presents the natural language assertions and corresponding rule patterns. It then describes the steps performed using

Evaluation of the mining techniques in constructing a traditional Chinese-language nursing recording system.

PubMed

Liao, Pei-Hung; Chu, William; Chu, Woei-Chyn

2014-05-01

In 2009, the Department of Health, part of Taiwan's Executive Yuan, announced the advent of electronic medical records to reduce medical expenses and facilitate the international exchange of medical record information. An information technology platform for nursing records in medical institutions was then quickly established, which improved nursing information systems and electronic databases. The purpose of the present study was to explore the usability of the data mining techniques to enhance completeness and ensure consistency of nursing records in the database system.First, the study used a Chinese word-segmenting system on common and special terms often used by the nursing staff. We also used text-mining techniques to collect keywords and create a keyword lexicon. We then used an association rule and artificial neural network to measure the correlation and forecasting capability for keywords. Finally, nursing staff members were provided with an on-screen pop-up menu to use when establishing nursing records. Our study found that by using mining techniques we were able to create a powerful keyword lexicon and establish a forecasting model for nursing diagnoses, ensuring the consistency of nursing terminology and improving the nursing staff's work efficiency and productivity.

75 FR 73995 - Lowering Miners' Exposure to Respirable Coal Mine Dust, Including Continuous Personal Dust Monitors

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-30

... http://www.msha.gov/REGS/FEDREG/PROPOSED/2010PROP/2010-25249.pdf . The proposed rule would revise the.../PROPOSED/2010PROP/2010-25249.pdf . The following error in the preamble to the proposed rule is corrected to...

Integrated mined-area reclamation and land-use planning. Volume 3C. A case study of surface mining and reclamation planning: Georgia Kaolin Company Clay Mines, Washington County, Georgia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guernsey, J L; Brown, L A; Perry, A O

1978-02-01

This case study examines the reclamation practices of the Georgia Kaolin's American Industrial Clay Company Division, a kaolin producer centered in Twiggs, Washington, and Wilkinson Counties, Georgia. The State of Georgia accounts for more than one-fourth of the world's kaolin production and about three-fourths of U.S. kaolin output. The mining of kaolin in Georgia illustrates the effects of mining and reclaiming lands disturbed by area surface mining. The disturbed areas are reclaimed under the rules and regulations of the Georgia Surface Mining Act of 1968. The natural conditions influencing the reclamation methodologies and techniques are markedly unique from those ofmore » other mining operations. The environmental disturbances and procedures used in reclaiming the kaolin mined lands are reviewed and implications for planners are noted.« less

78 FR 5055 - Pattern of Violations

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-23

...The Mine Safety and Health Administration (MSHA) is revising the Agency's existing regulation for pattern of violations (POV). MSHA has determined that the existing regulation does not adequately achieve the intent of the Federal Mine Safety and Health Act of 1977 (Mine Act) that the POV provision be used to address mine operators who have demonstrated a disregard for the health and safety of miners. Congress included the POV provision in the Mine Act so that mine operators would manage health and safety conditions at mines and find and fix the root causes of significant and substantial (S&S) violations, protecting the health and safety of miners. The final rule simplifies the existing POV criteria, improves consistency in applying the POV criteria, and more effectively achieves the Mine Act's statutory intent. It also encourages chronic safety violators to comply with the Mine Act and MSHA's health and safety standards.

Mining Association Rules Between Credits in the Leadership in Energy and Environmental Design for New Construction (LEED-NC) Green Building Assessment System

DTIC Science & Technology

2008-03-01

in subject areas that rely mostly on intuition, like marketing, sales , and customer relationship management (Berry and Linoff, 2004). Commonly...closely related to this study might be Amazon or iTunes ’ use of market basket analysis. Today, most e-commerce consumers are accustomed to receiving... sales is to minimize the costs and hassle of warranty-related repairs and replacements. Of course, the best way to minimize those liabilities is to

Mining TCGA Data Using Boolean Implications

PubMed Central

Sinha, Subarna; Tsang, Emily K.; Zeng, Haoyang; Meister, Michela; Dill, David L.

2014-01-01

Boolean implications (if-then rules) provide a conceptually simple, uniform and highly scalable way to find associations between pairs of random variables. In this paper, we propose to use Boolean implications to find relationships between variables of different data types (mutation, copy number alteration, DNA methylation and gene expression) from the glioblastoma (GBM) and ovarian serous cystadenoma (OV) data sets from The Cancer Genome Atlas (TCGA). We find hundreds of thousands of Boolean implications from these data sets. A direct comparison of the relationships found by Boolean implications and those found by commonly used methods for mining associations show that existing methods would miss relationships found by Boolean implications. Furthermore, many relationships exposed by Boolean implications reflect important aspects of cancer biology. Examples of our findings include cis relationships between copy number alteration, DNA methylation and expression of genes, a new hierarchy of mutations and recurrent copy number alterations, loss-of-heterozygosity of well-known tumor suppressors, and the hypermethylation phenotype associated with IDH1 mutations in GBM. The Boolean implication results used in the paper can be accessed at http://crookneck.stanford.edu/microarray/TCGANetworks/. PMID:25054200

Application of data mining in science and technology management information system based on WebGIS

NASA Astrophysics Data System (ADS)

Wu, Xiaofang; Xu, Zhiyong; Bao, Shitai; Chen, Feixiang

2009-10-01

With the rapid development of science and technology and the quick increase of information, a great deal of data is accumulated in the management department of science and technology. Usually, many knowledge and rules are contained and concealed in the data. Therefore, how to excavate and use the knowledge fully is very important in the management of science and technology. It will help to examine and approve the project of science and technology more scientifically and make the achievement transformed as the realistic productive forces easier. Therefore, the data mine technology will be researched and applied to the science and technology management information system to find and excavate the knowledge in the paper. According to analyzing the disadvantages of traditional science and technology management information system, the database technology, data mining and web geographic information systems (WebGIS) technology will be introduced to develop and construct the science and technology management information system based on WebGIS. The key problems are researched in detail such as data mining and statistical analysis. What's more, the prototype system is developed and validated based on the project data of National Natural Science Foundation Committee. The spatial data mining is done from the axis of time, space and other factors. Then the variety of knowledge and rules will be excavated by using data mining technology, which helps to provide an effective support for decisionmaking.

[Ginseng prescription rules and molecular mechanism in treating coronary heart disease based on data mining and integrative pharmacology].

PubMed

Li, Sen; Tang, Shi-Huan; Liu, Jin-Ling; Su, Jin; He, Fu-Yuan

2018-04-01

The ancient dragon Materia Medica, Compendium of Materia Medica and other works recorded that the main effect of ginseng is tonifying qi. It is reported that the main active ingredient of ginseng is ginsenoside. Modern studies have found that ginseng mono saponins are effective for cardiovascular related diseases. This paper preliminary clarified the efficacy of traditional ginseng-nourishing qi and cardiovascular disease through the traditional Chinese medicine (TCM) inheritance auxiliary platform and integration platform of association of pharmacology. With the help of TCM inheritance auxiliary platform-analysis of "Chinese medicine database", Chinese medicine treatment of modern diseases that ginseng rules, so the traditional effect associated with modern medicine and pharmacology; application integration platform enrichment analysis on the target of drug and gene function, metabolic pathway, to further explore the molecular mechanism of ginseng in the treatment of coronary heart disease, aimed at mining the molecular mechanism of ginseng in the treatment of coronary heart disease. Chinese medicine containing ginseng 307 prescriptions, 87 kinds of disease indications, western medicine disease Chinese medicine therapy for ginseng main coronary heart disease; analysis of molecular mechanism of ginseng pharmacology integration platform for the treatment of coronary heart disease. Ginsenosides（Ra₁, Ra₂, Rb₁, Rb₂, Rg₁, Ro) bind these targets, PRKAA1, PRKAA2, NDUFA4, COX5B, UQCRC1, affect chemokines, non-alcoholic fatty liver, gonadotropin, carbon metabolism, glucose metabolism and other pathways to treat coronary heart disease indirectly. The molecular mechanism of Panax ginseng's multi-component, multi-target and synergistic action is preliminarily elucidated in this paper. Copyright© by the Chinese Pharmaceutical Association.

76 FR 6110 - Mine Safety Disclosure

Federal Register 2010, 2011, 2012, 2013, 2014

2011-02-03

... Comments Use the Commission's Internet comment form ( http://www.sec.gov/rules/proposed.shtml ); Send an e... all comments on the Commission's Internet Web site ( http://www.sec.gov/rules/proposed.shtml... on the proposal to, among other things, allow for the collection of information and improve the...

30 CFR 937.700 - Oregon Federal program.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 30 Mineral Resources 3 2012-07-01 2012-07-01 false Oregon Federal program. 937.700 Section 937.700... PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE OREGON § 937.700 Oregon Federal program. (a) This part contains all rules that are applicable to surface coal mining operations in Oregon...

30 CFR 937.700 - Oregon Federal program.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 3 2011-07-01 2011-07-01 false Oregon Federal program. 937.700 Section 937.700... PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE OREGON § 937.700 Oregon Federal program. (a) This part contains all rules that are applicable to surface coal mining operations in Oregon...

30 CFR 937.700 - Oregon Federal program.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 30 Mineral Resources 3 2014-07-01 2014-07-01 false Oregon Federal program. 937.700 Section 937.700... PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE OREGON § 937.700 Oregon Federal program. (a) This part contains all rules that are applicable to surface coal mining operations in Oregon...

76 FR 12648 - Lowering Miners' Exposure to Respirable Coal Mine Dust, Including Continuous Personal Dust Monitors

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-08

... be appropriate to use on a short-term basis. 13. The proposed rule addresses (1) which occupations... for respirable coal mine dust, provide for full- shift sampling, redefine the term ``normal production... respect to their availability. If shorter or longer timeframes are recommended, please provide the...

[Analysis of the characteristics of the older adults with depression using data mining decision tree analysis].

PubMed

Park, Myonghwa; Choi, Sora; Shin, A Mi; Koo, Chul Hoi

2013-02-01

The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method. A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs. The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease. The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.

30 CFR 910.817 - Performance standards-underground mining activities.

Code of Federal Regulations, 2013 CFR

2013-07-01

... with the Georgia Safe Dams Act and Rules for Safety of the Natural Resources, Environmental Protection Division; the Solid Waste Management Rules of the Georgia Department of Natural Resources, Environmental Protection Division, Chapter 391-3-4; and the Georgia Seed Laws and Regulation 4. [47 FR 36399, Aug. 19, 1982...

30 CFR 910.816 - Performance standards-surface mining activities.

Code of Federal Regulations, 2013 CFR

2013-07-01

... except in compliance with the Georgia Safe Dams Act and Rules for Safety of the Natural Resources, Environmental Protection Division; the Solid Waste Management Rules of the Georgia Department of Natural Resources, Environmental Protection Division, Chapter 391-3-4; and the Georgia Seed Laws and Regulation 4...

30 CFR 910.817 - Performance standards-underground mining activities.

Code of Federal Regulations, 2014 CFR

2014-07-01

... with the Georgia Safe Dams Act and Rules for Safety of the Natural Resources, Environmental Protection Division; the Solid Waste Management Rules of the Georgia Department of Natural Resources, Environmental Protection Division, Chapter 391-3-4; and the Georgia Seed Laws and Regulation 4. [47 FR 36399, Aug. 19, 1982...

30 CFR 910.817 - Performance standards-underground mining activities.

Code of Federal Regulations, 2012 CFR

2012-07-01

... with the Georgia Safe Dams Act and Rules for Safety of the Natural Resources, Environmental Protection Division; the Solid Waste Management Rules of the Georgia Department of Natural Resources, Environmental Protection Division, Chapter 391-3-4; and the Georgia Seed Laws and Regulation 4. [47 FR 36399, Aug. 19, 1982...

30 CFR 910.816 - Performance standards-surface mining activities.

Code of Federal Regulations, 2012 CFR

2012-07-01

... except in compliance with the Georgia Safe Dams Act and Rules for Safety of the Natural Resources, Environmental Protection Division; the Solid Waste Management Rules of the Georgia Department of Natural Resources, Environmental Protection Division, Chapter 391-3-4; and the Georgia Seed Laws and Regulation 4...

30 CFR 910.816 - Performance standards-surface mining activities.

Code of Federal Regulations, 2014 CFR

2014-07-01

... except in compliance with the Georgia Safe Dams Act and Rules for Safety of the Natural Resources, Environmental Protection Division; the Solid Waste Management Rules of the Georgia Department of Natural Resources, Environmental Protection Division, Chapter 391-3-4; and the Georgia Seed Laws and Regulation 4...

Sustainability Activities In The Mining Sector: Current Status And Challenges Ahead Limestone Mining In Nusakambangan

NASA Astrophysics Data System (ADS)

Ayuningrum, Theresia Vika; Purnaweni, Hartuti

2018-02-01

Potential Karst area in Nusakambangan has an important role in maintaining the balance of nature. But with the existence of mining activities, will automatically change the environmental conditions there. In order for the utilization of resources to meet the rules of optimization between the interests of mining and sustainability of the environment so in every mining sector activities required a variety of environmental studies. The purpose of this study is to find out how the analysis of environmental management due to limestone mining activities in Nusakambangan so that it can be known the management of mining areas are optimal, wise based on ecological principles, and sustainability. In qualitative research methods, data analysis using description percentage, with the type of data collected in the form of primary data and secondary data.

[Prescription rules of preparations containing Crataegi Fructus in Chinese patent drug].

PubMed

Geng, Ya; Ma, Yue-Xiang; Xu, Hai-Yu; Li, Jun-Fang; Tang, Shi-Huan; Yang, Hong-Jun

2016-08-01

To analyze the prescription rules of preparations containing Crataegi Fructus in the drug standards of the People's Republic of China Ministry of Public Health-Chinese Patent Drug(hereinafter referred to as Chinese patent drug), and provide some references for clinical application and the research and development of new medicines. Based on TCMISS(V2.5), the prescriptions containing Crataegi Fructus in Chinese patent drug were collected to build the database; association rules, frequency statistics and other data mining methods were used to analyze the disease syndrome, common drug compatibility and prescription rules. There were a total of 308 prescriptions containing Crataegi Fructus, involving 499 kinds of Chinese medicines, 34 commonly used drug combinations, and mainly for 18 kinds of diseases. Drug combination analysis was done with "Crataegi Fructus-Citri Reticulatae Pericarpium" and "Crataegi Fructus-Poria" as the high-frequency herb pairs and with "stagnation" and "diarrhea" as the high-frequency diseases. The results indicated that the Crataegi Fructus in different herb pairs had a roughly same function, and its therapy effect was different in different diseases. The prescriptions containing Crataegi Fructus in Chinese patent drug had the effect of digestion, and they were widely used in clinical application, often used together with spleen-strengthening medicines to achieve different treatment effects; the prescription rules reflected the prescription characteristics of Crataegi Fructus for different diseases, providing a basis for its clinically scientific application and the research and development of new medicines. Copyright© by the Chinese Pharmaceutical Association.

Assessment of corn and banana leaves as potential standardized substrates for leaf decomposition in streams affected by mountaintop removal coal mining, West Virginia, USA

EPA Science Inventory

Mountaintop removal and valley filling is a method of coal mining that buries Central Appalachian headwater streams. A 2007 federal court ruling highlighted the need for measurement of both ecosystem structure and function when assessing streams for mitigaton. Rapid functional as...

A modeling of dynamic storage assignment for order picking in beverage warehousing with Drive-in Rack system

NASA Astrophysics Data System (ADS)

Hadi, M. Z.; Djatna, T.; Sugiarto

2018-04-01

This paper develops a dynamic storage assignment model to solve storage assignment problem (SAP) for beverages order picking in a drive-in rack warehousing system to determine the appropriate storage location and space for each beverage products dynamically so that the performance of the system can be improved. This study constructs a graph model to represent drive-in rack storage position then combine association rules mining, class-based storage policies and an arrangement rule algorithm to determine an appropriate storage location and arrangement of the product according to dynamic orders from customers. The performance of the proposed model is measured as rule adjacency accuracy, travel distance (for picking process) and probability a product become expiry using Last Come First Serve (LCFS) queue approach. Finally, the proposed model is implemented through computer simulation and compare the performance for different storage assignment methods as well. The result indicates that the proposed model outperforms other storage assignment methods.

Improve Data Mining and Knowledge Discovery Through the Use of MatLab

NASA Technical Reports Server (NTRS)

Shaykhian, Gholam Ali; Martin, Dawn (Elliott); Beil, Robert

2011-01-01

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(R) (MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Improve Data Mining and Knowledge Discovery through the use of MatLab

NASA Technical Reports Server (NTRS)

Shaykahian, Gholan Ali; Martin, Dawn Elliott; Beil, Robert

2011-01-01

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(TradeMark)(MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Fuzzy association rule mining and classification for the prediction of malaria in South Korea.

PubMed

Buczak, Anna L; Baugher, Benjamin; Guven, Erhan; Ramac-Thomas, Liane C; Elbert, Yevgeniy; Babin, Steven M; Lewis, Sheri H

2015-06-18

Malaria is the world's most prevalent vector-borne disease. Accurate prediction of malaria outbreaks may lead to public health interventions that mitigate disease morbidity and mortality. We describe an application of a method for creating prediction models utilizing Fuzzy Association Rule Mining to extract relationships between epidemiological, meteorological, climatic, and socio-economic data from Korea. These relationships are in the form of rules, from which the best set of rules is automatically chosen and forms a classifier. Two classifiers have been built and their results fused to become a malaria prediction model. Future malaria cases are predicted as Low, Medium or High, where these classes are defined as a total of 0-2, 3-16, and above 17 cases, respectively, for a region in South Korea during a two-week period. Based on user recommendations, HIGH is considered an outbreak. Model accuracy is described by Positive Predictive Value (PPV), Sensitivity, and F-score for each class, computed on test data not previously used to develop the model. For predictions made 7-8 weeks in advance, model PPV and Sensitivity are 0.842 and 0.681, respectively, for the HIGH classes. The F0.5 and F3 scores (which combine PPV and Sensitivity) are 0.804 and 0.694, respectively, for the HIGH classes. The overall FARM results (as measured by F-scores) are significantly better than those obtained by Decision Tree, Random Forest, Support Vector Machine, and Holt-Winters methods for the HIGH class. For the Medium class, Random Forest and FARM obtain comparable results, with FARM being better at F0.5, and Random Forest obtaining a higher F3. A previously described method for creating disease prediction models has been modified and extended to build models for predicting malaria. In addition, some new input variables were used, including indicators of intervention measures. The South Korea malaria prediction models predict Low, Medium or High cases 7-8 weeks in the future. This paper demonstrates that our data driven approach can be used for the prediction of different diseases.

The Royal Navy and British Security Policy.

DTIC Science & Technology

1983-12-01

supremacy were embodied in that fleet. Britannia ruled the waves around the world. -~ Sixty-six years later Rear Admiral Sandy Woodward went *into battle off...already sold to Australia and just over a dozen destroyers and frigates. Britannia ruled the waves around those remote islands only with great difficulty...with the Americans, vulnerability to mining and the costs in manpower and money that a larger force would require, ruled out the non-nuclear-powered

75 FR 17803 - Self-Regulatory Organizations; NASDAQ OMX PHLX, Inc.; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-07

.... The text of the proposed rule change is available on the Exchange's Web site at http://nasdaqtrader... and discussed any comments it received on the proposed rule change. The text of these statements may... Mining Corporation (``NEM''); Palm, Inc. (``PALM''); Pfizer, Inc. (``PFE''); ''); Potash Corp...

29 CFR 2700.55 - Powers of Judges.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 29 Labor 9 2013-07-01 2013-07-01 false Powers of Judges. 2700.55 Section 2700.55 Labor Regulations Relating to Labor (Continued) FEDERAL MINE SAFETY AND HEALTH REVIEW COMMISSION PROCEDURAL RULES Hearings § 2700.55 Powers of Judges. Subject to these rules, a Judge is empowered to: (a) Administer oaths and...

29 CFR 2700.55 - Powers of Judges.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 29 Labor 9 2012-07-01 2012-07-01 false Powers of Judges. 2700.55 Section 2700.55 Labor Regulations Relating to Labor (Continued) FEDERAL MINE SAFETY AND HEALTH REVIEW COMMISSION PROCEDURAL RULES Hearings § 2700.55 Powers of Judges. Subject to these rules, a Judge is empowered to: (a) Administer oaths and...

29 CFR 2700.55 - Powers of Judges.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 29 Labor 9 2014-07-01 2014-07-01 false Powers of Judges. 2700.55 Section 2700.55 Labor Regulations Relating to Labor (Continued) FEDERAL MINE SAFETY AND HEALTH REVIEW COMMISSION PROCEDURAL RULES Hearings § 2700.55 Powers of Judges. Subject to these rules, a Judge is empowered to: (a) Administer oaths and...

20 CFR 410.687 - Rules governing the representation and advising of claimants and parties.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 20 Employees' Benefits 2 2010-04-01 2010-04-01 false Rules governing the representation and advising of claimants and parties. 410.687 Section 410.687 Employees' Benefits SOCIAL SECURITY ADMINISTRATION FEDERAL COAL MINE HEALTH AND SAFETY ACT OF 1969, TITLE IV-BLACK LUNG BENEFITS (1969...

[Traditional Chinese medicine inheritance system analysis of professor Ding Yuanqing in treating tic disorder medication based on experience].

PubMed

Sun, Lu-yan; Li, Qing-peng; Zhao, Li-li; Ding, Yuan-qing

2015-08-01

In recent years, the incidence of tic disorders has increased, and it is not uncommon for the patients to treat the disease. The pathogenesis and pathogenesis of Western medicine are not yet clear, the clinical commonly used western medicine has many adverse reactions, traditional Chinese medicine (TCM) research is increasingly valued. Based on the software of TCM inheritance assistant system, this paper discusses Ding Yuanqing's experience in treating tic disorder with Professor. Collect yuan Qing Ding professor in treating tic disorder of medical records by association rules Apriori algorithm, complex system entropy clustering without supervision and data mining method, carries on the analysis to the selected 800 prescriptions, to determine the frequency of use of prescription drugs, the association rules between the drug and digging out the 12 core combination and the first six new prescription, medication transferred to the liver and extinguish wind, cooling blood and relieving convulsion, Qingxin soothe the nerves, with the card cut, flexible application, strict compatibility.

Occupancy schedules learning process through a data mining framework

DOE Office of Scientific and Technical Information (OSTI.GOV)

D'Oca, Simona; Hong, Tianzhen

Building occupancy is a paramount factor in building energy simulations. Specifically, lighting, plug loads, HVAC equipment utilization, fresh air requirements and internal heat gain or loss greatly depends on the level of occupancy within a building. Developing the appropriate methodologies to describe and reproduce the intricate network responsible for human-building interactions are needed. Extrapolation of patterns from big data streams is a powerful analysis technique which will allow for a better understanding of energy usage in buildings. A three-step data mining framework is applied to discover occupancy patterns in office spaces. First, a data set of 16 offices with 10more » minute interval occupancy data, over a two year period is mined through a decision tree model which predicts the occupancy presence. Then a rule induction algorithm is used to learn a pruned set of rules on the results from the decision tree model. Finally, a cluster analysis is employed in order to obtain consistent patterns of occupancy schedules. Furthermore, the identified occupancy rules and schedules are representative as four archetypal working profiles that can be used as input to current building energy modeling programs, such as EnergyPlus or IDA-ICE, to investigate impact of occupant presence on design, operation and energy use in office buildings.« less

Occupancy schedules learning process through a data mining framework

DOE PAGES

D'Oca, Simona; Hong, Tianzhen

2014-12-17

Building occupancy is a paramount factor in building energy simulations. Specifically, lighting, plug loads, HVAC equipment utilization, fresh air requirements and internal heat gain or loss greatly depends on the level of occupancy within a building. Developing the appropriate methodologies to describe and reproduce the intricate network responsible for human-building interactions are needed. Extrapolation of patterns from big data streams is a powerful analysis technique which will allow for a better understanding of energy usage in buildings. A three-step data mining framework is applied to discover occupancy patterns in office spaces. First, a data set of 16 offices with 10more » minute interval occupancy data, over a two year period is mined through a decision tree model which predicts the occupancy presence. Then a rule induction algorithm is used to learn a pruned set of rules on the results from the decision tree model. Finally, a cluster analysis is employed in order to obtain consistent patterns of occupancy schedules. Furthermore, the identified occupancy rules and schedules are representative as four archetypal working profiles that can be used as input to current building energy modeling programs, such as EnergyPlus or IDA-ICE, to investigate impact of occupant presence on design, operation and energy use in office buildings.« less

Predicting biomedical metadata in CEDAR: A study of Gene Expression Omnibus (GEO).

PubMed

Panahiazar, Maryam; Dumontier, Michel; Gevaert, Olivier

2017-08-01

A crucial and limiting factor in data reuse is the lack of accurate, structured, and complete descriptions of data, known as metadata. Towards improving the quantity and quality of metadata, we propose a novel metadata prediction framework to learn associations from existing metadata that can be used to predict metadata values. We evaluate our framework in the context of experimental metadata from the Gene Expression Omnibus (GEO). We applied four rule mining algorithms to the most common structured metadata elements (sample type, molecular type, platform, label type and organism) from over 1.3million GEO records. We examined the quality of well supported rules from each algorithm and visualized the dependencies among metadata elements. Finally, we evaluated the performance of the algorithms in terms of accuracy, precision, recall, and F-measure. We found that PART is the best algorithm outperforming Apriori, Predictive Apriori, and Decision Table. All algorithms perform significantly better in predicting class values than the majority vote classifier. We found that the performance of the algorithms is related to the dimensionality of the GEO elements. The average performance of all algorithm increases due of the decreasing of dimensionality of the unique values of these elements (2697 platforms, 537 organisms, 454 labels, 9 molecules, and 5 types). Our work suggests that experimental metadata such as present in GEO can be accurately predicted using rule mining algorithms. Our work has implications for both prospective and retrospective augmentation of metadata quality, which are geared towards making data easier to find and reuse. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

[Data mining analysis of regularity of formulas containing Salviae Miltiorrhizae Radix et Rhizoma-Carthami Flos medicin pair in Dictionary of Chinese Medicine Prescription].

PubMed

Cui, Yi-Ran; Liu, Xin; Shen, Dan; Yang, Hong-Jun; Tang, Shi-Huan

2016-02-01

In this study, formulas containing Salviae Miltiorrhizae Radix et Rhizoma-Carthami Flos in the database of Dictionary of Chinese Medicine Prescription (DCMP) were extracted by using traditional Chinese medicine inheritance support system (TCMISS). The drugs pairs and formula composition rules were analyzed with data mining methods, such as association rules, improved mutual information method and complex system entropy clustering. Totally 39 formulas were included in this study and involved 280 Chinese medicines. The top 5 Chinese medicines most frequently used were Danggui (Angelica sinensis), Chuanxiong (Ligusticum chuanxiong), Xiangfu (Cyperi Rhizoma), Baishao(Radix Paeoniae Alba), Taoren(Prunus persica) and Shengdihuang (Radix Rehmanniae Recens). Six core medicinal pairs were obtained through clustering analysis, namely Danshen (Salviae Miltiorrhizae Radix et Rhizoma)-Xiangfu (Cyperi Rhizoma)-Honghua (Carthami Flos), Danshen (Salviae Miltiorrhizae Radix et Rhizoma)-Baishao (Radix Paeoniae Alba)-Honghua (Carthami Flos), Danshen (Salviae Miltiorrhizae Radix et Rhizoma)-Danggui (A. sinensis)-Xiagnfu (Cyperi Rhizoma)-Honghua (Carthami Flos), Danshen (Salviae Miltiorrhizae Radix et Rhizoma)-Danggui (A. sinensis)-Baishao (Radix Paeoniae Alba)-Honghua (Carthami Flos), Honghua (Carthami Flos)-Danshen (Salviae Miltiorrhizae Radix et Rhizoma)-Baishao (Radix Paeoniae Alba)-Danggui (A. sinensis), Danshen (Salviae Miltiorrhizae Radix et Rhizoma)-Baishao (Radix Paeoniae Alba)-Honghua (Carthami Flos)-Danggui (A. sinensis). The support degree was set at 11 (38.46%), with a confidence coefficient of 80%, and then 38 associated pairs were screened. These results suggested that Salviae Miltiorrhizae Radix et Rhizoma, Carthami Flos is often combined with herbs for activating blood and promoting circulation of qi to treat gynecopathy, stasis blood pain syndrome, stroke and other syndromes. Copyright© by the Chinese Pharmaceutical Association.

Decision Tree based Prediction and Rule Induction for Groundwater Trichloroethene (TCE) Pollution Vulnerability

NASA Astrophysics Data System (ADS)

Park, J.; Yoo, K.

2013-12-01

For groundwater resource conservation, it is important to accurately assess groundwater pollution sensitivity or vulnerability. In this work, we attempted to use data mining approach to assess groundwater pollution vulnerability in a TCE (trichloroethylene) contaminated Korean industrial site. The conventional DRASTIC method failed to describe TCE sensitivity data with a poor correlation with hydrogeological properties. Among the different data mining methods such as Artificial Neural Network (ANN), Multiple Logistic Regression (MLR), Case Base Reasoning (CBR), and Decision Tree (DT), the accuracy and consistency of Decision Tree (DT) was the best. According to the following tree analyses with the optimal DT model, the failure of the conventional DRASTIC method in fitting with TCE sensitivity data may be due to the use of inaccurate weight values of hydrogeological parameters for the study site. These findings provide a proof of concept that DT based data mining approach can be used in predicting and rule induction of groundwater TCE sensitivity without pre-existing information on weights of hydrogeological properties.

The Usage of Association Rule Mining to Identify Influencing Factors on Deafness After Birth.

PubMed

Shahraki, Azimeh Danesh; Safdari, Reza; Gahfarokhi, Hamid Habibi; Tahmasebian, Shahram

2015-12-01

Providing complete and high quality health care services has very important role to enable people to understand the factors related to personal and social health and to make decision regarding choice of suitable healthy behaviors in order to achieve healthy life. For this reason, demographic and clinical data of person are collecting, this huge volume of data can be known as a valuable resource for analyzing, exploring and discovering valuable information and communication. This study using forum rules techniques in the data mining has tried to identify the affecting factors on hearing loss after birth in Iran. The survey is kind of data oriented study. The population of the study is contained questionnaires in several provinces of the country. First, all data of questionnaire was implemented in the form of information table in Software SQL Server and followed by Data Entry using written software of C # .Net, then algorithm Association in SQL Server Data Tools software and Clementine software was implemented to determine the rules and hidden patterns in the gathered data. Two factors of number of deaf brothers and the degree of consanguinity of the parents have a significant impact on severity of deafness of individuals. Also, when the severity of hearing loss is greater than or equal to moderately severe hearing loss, people use hearing aids and Men are also less interested in the use of hearing aids. In fact, it can be said that in families with consanguineous marriage of parents that are from first degree (girl/boy cousins) and 2(nd) degree relatives (girl/boy cousins) and especially from first degree, the number of people with severe hearing loss or deafness are more and in the use of hearing aids, gender of the patient is more important than the severity of the hearing loss.

Study on the Rule of Super Strata Movement and Subsidence

NASA Astrophysics Data System (ADS)

Yao, Shunli; Yuan, Hongyong; Jiang, Fuxing; Chen, Tao; Wu, Peng

2018-01-01

The movement of key strata is related to the safety of the whole earth’s surface for coal mining under super strata. Based on the key strata theory, the paper comprehensively analyzes the characteristics of the subsidence before and after the instability of the super strata by studing through FLAC3D and microseismic dynamic monitoring of the surface rock movement observation. The stability of the super strata movement is analyzed according to the characteristic value of the subsidence. The subsidence law and quantitative indexes under the control of the super rock strata that provides basis for the prevention and control of surface risk, optimize mining area and face layout and reasonably set mining boundary around mining area. It provides basis for the even growth of mine safety production and regional public safety.

The Weather Forecast Using Data Mining Research Based on Cloud Computing.

NASA Astrophysics Data System (ADS)

Wang, ZhanJie; Mazharul Mujib, A. B. M.

2017-10-01

Weather forecasting has been an important application in meteorology and one of the most scientifically and technologically challenging problem around the world. In my study, we have analyzed the use of data mining techniques in forecasting weather. This paper proposes a modern method to develop a service oriented architecture for the weather information systems which forecast weather using these data mining techniques. This can be carried out by using Artificial Neural Network and Decision tree Algorithms and meteorological data collected in Specific time. Algorithm has presented the best results to generate classification rules for the mean weather variables. The results showed that these data mining techniques can be enough for weather forecasting.

The Value of Data Mining in Music Education Research and Some Findings from Its Application to a Study of Instrumental Learning during Childhood

ERIC Educational Resources Information Center

Faulkner, Robert; Davidson, Jane W.; McPherson, Gary E.

2010-01-01

The use of data mining for the analysis of data collected in natural settings is increasingly recognized as a legitimate mode of enquiry. This rule-inductive paradigm is an effective means of discovering relationships within large datasets--especially in research that has limited experimental design--and for the subsequent formulation of…

MINEs: Open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics

DOE PAGES

Jeffryes, James G.; Colastani, Ricardo L.; Elbadawi-Sidhu, Mona; ...

2015-08-28

Metabolomics have proven difficult to execute in an untargeted and generalizable manner. Liquid chromatography–mass spectrometry (LC–MS) has made it possible to gather data on thousands of cellular metabolites. However, matching metabolites to their spectral features continues to be a bottleneck, meaning that much of the collected information remains uninterpreted and that new metabolites are seldom discovered in untargeted studies. These challenges require new approaches that consider compounds beyond those available in curated biochemistry databases. Here we present Metabolic In silico Network Expansions (MINEs), an extension of known metabolite databases to include molecules that have not been observed, but are likelymore » to occur based on known metabolites and common biochemical reactions. We utilize an algorithm called the Biochemical Network Integrated Computational Explorer (BNICE) and expert-curated reaction rules based on the Enzyme Commission classification system to propose the novel chemical structures and reactions that comprise MINE databases. Starting from the Kyoto Encyclopedia of Genes and Genomes (KEGG) COMPOUND database, the MINE contains over 571,000 compounds, of which 93% are not present in the PubChem database. However, these MINE compounds have on average higher structural similarity to natural products than compounds from KEGG or PubChem. MINE databases were able to propose annotations for 98.6% of a set of 667 MassBank spectra, 14% more than KEGG alone and equivalent to PubChem while returning far fewer candidates per spectra than PubChem (46 vs. 1715 median candidates). Application of MINEs to LC–MS accurate mass data enabled the identity of an unknown peak to be confidently predicted. MINE databases are freely accessible for non-commercial use via user-friendly web-tools at http://minedatabase.mcs.anl.gov and developer-friendly APIs. MINEs improve metabolomics peak identification as compared to general chemical databases whose results include irrelevant synthetic compounds. MINEs complement and expand on previous in silico generated compound databases that focus on human metabolism. We are actively developing the database; future versions of this resource will incorporate transformation rules for spontaneous chemical reactions and more advanced filtering and prioritization of candidate structures.« less

A novel on-line spatial-temporal k-anonymity method for location privacy protection from sequence rules-based inference attacks.

PubMed

Zhang, Haitao; Wu, Chenxue; Chen, Zewei; Liu, Zhao; Zhu, Yunhong

2017-01-01

Analyzing large-scale spatial-temporal k-anonymity datasets recorded in location-based service (LBS) application servers can benefit some LBS applications. However, such analyses can allow adversaries to make inference attacks that cannot be handled by spatial-temporal k-anonymity methods or other methods for protecting sensitive knowledge. In response to this challenge, first we defined a destination location prediction attack model based on privacy-sensitive sequence rules mined from large scale anonymity datasets. Then we proposed a novel on-line spatial-temporal k-anonymity method that can resist such inference attacks. Our anti-attack technique generates new anonymity datasets with awareness of privacy-sensitive sequence rules. The new datasets extend the original sequence database of anonymity datasets to hide the privacy-sensitive rules progressively. The process includes two phases: off-line analysis and on-line application. In the off-line phase, sequence rules are mined from an original sequence database of anonymity datasets, and privacy-sensitive sequence rules are developed by correlating privacy-sensitive spatial regions with spatial grid cells among the sequence rules. In the on-line phase, new anonymity datasets are generated upon LBS requests by adopting specific generalization and avoidance principles to hide the privacy-sensitive sequence rules progressively from the extended sequence anonymity datasets database. We conducted extensive experiments to test the performance of the proposed method, and to explore the influence of the parameter K value. The results demonstrated that our proposed approach is faster and more effective for hiding privacy-sensitive sequence rules in terms of hiding sensitive rules ratios to eliminate inference attacks. Our method also had fewer side effects in terms of generating new sensitive rules ratios than the traditional spatial-temporal k-anonymity method, and had basically the same side effects in terms of non-sensitive rules variation ratios with the traditional spatial-temporal k-anonymity method. Furthermore, we also found the performance variation tendency from the parameter K value, which can help achieve the goal of hiding the maximum number of original sensitive rules while generating a minimum of new sensitive rules and affecting a minimum number of non-sensitive rules.

A novel on-line spatial-temporal k-anonymity method for location privacy protection from sequence rules-based inference attacks

PubMed Central

Wu, Chenxue; Liu, Zhao; Zhu, Yunhong

2017-01-01

Analyzing large-scale spatial-temporal k-anonymity datasets recorded in location-based service (LBS) application servers can benefit some LBS applications. However, such analyses can allow adversaries to make inference attacks that cannot be handled by spatial-temporal k-anonymity methods or other methods for protecting sensitive knowledge. In response to this challenge, first we defined a destination location prediction attack model based on privacy-sensitive sequence rules mined from large scale anonymity datasets. Then we proposed a novel on-line spatial-temporal k-anonymity method that can resist such inference attacks. Our anti-attack technique generates new anonymity datasets with awareness of privacy-sensitive sequence rules. The new datasets extend the original sequence database of anonymity datasets to hide the privacy-sensitive rules progressively. The process includes two phases: off-line analysis and on-line application. In the off-line phase, sequence rules are mined from an original sequence database of anonymity datasets, and privacy-sensitive sequence rules are developed by correlating privacy-sensitive spatial regions with spatial grid cells among the sequence rules. In the on-line phase, new anonymity datasets are generated upon LBS requests by adopting specific generalization and avoidance principles to hide the privacy-sensitive sequence rules progressively from the extended sequence anonymity datasets database. We conducted extensive experiments to test the performance of the proposed method, and to explore the influence of the parameter K value. The results demonstrated that our proposed approach is faster and more effective for hiding privacy-sensitive sequence rules in terms of hiding sensitive rules ratios to eliminate inference attacks. Our method also had fewer side effects in terms of generating new sensitive rules ratios than the traditional spatial-temporal k-anonymity method, and had basically the same side effects in terms of non-sensitive rules variation ratios with the traditional spatial-temporal k-anonymity method. Furthermore, we also found the performance variation tendency from the parameter K value, which can help achieve the goal of hiding the maximum number of original sensitive rules while generating a minimum of new sensitive rules and affecting a minimum number of non-sensitive rules. PMID:28767687

26 CFR 1.367(a)-4T - Special rules applicable to specified transfers of property (temporary).

Code of Federal Regulations, 2010 CFR

2010-04-01

... property (as defined in paragraph (b)(2) of this section) to a foreign corporation in an exchange described... subject to the rules of this paragraph (b) is any property that— (i) Is either mining property (as defined in section 617(f)(2)), section 1245 property (as defined in section 1245(a)(3)), section 1250...

40 CFR 52.1222 - Original Identification of plan section.

Code of Federal Regulations, 2010 CFR

2010-07-01

... between the State Pollution Control Agency and Erie Mining Company submitted by the State on February 20... 19, 1983, at 8 S.R. 1419 (text of rule starting at 8 S.R. 1420) and adopted as modified on April 16... Permits—Proposed and Published on December 19, 1983, at 8 S.R. 1419 (text of rule starting at 8 S.R. 1470...

MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics.

PubMed

Jeffryes, James G; Colastani, Ricardo L; Elbadawi-Sidhu, Mona; Kind, Tobias; Niehaus, Thomas D; Broadbelt, Linda J; Hanson, Andrew D; Fiehn, Oliver; Tyo, Keith E J; Henry, Christopher S

2015-01-01

In spite of its great promise, metabolomics has proven difficult to execute in an untargeted and generalizable manner. Liquid chromatography-mass spectrometry (LC-MS) has made it possible to gather data on thousands of cellular metabolites. However, matching metabolites to their spectral features continues to be a bottleneck, meaning that much of the collected information remains uninterpreted and that new metabolites are seldom discovered in untargeted studies. These challenges require new approaches that consider compounds beyond those available in curated biochemistry databases. Here we present Metabolic In silico Network Expansions (MINEs), an extension of known metabolite databases to include molecules that have not been observed, but are likely to occur based on known metabolites and common biochemical reactions. We utilize an algorithm called the Biochemical Network Integrated Computational Explorer (BNICE) and expert-curated reaction rules based on the Enzyme Commission classification system to propose the novel chemical structures and reactions that comprise MINE databases. Starting from the Kyoto Encyclopedia of Genes and Genomes (KEGG) COMPOUND database, the MINE contains over 571,000 compounds, of which 93% are not present in the PubChem database. However, these MINE compounds have on average higher structural similarity to natural products than compounds from KEGG or PubChem. MINE databases were able to propose annotations for 98.6% of a set of 667 MassBank spectra, 14% more than KEGG alone and equivalent to PubChem while returning far fewer candidates per spectra than PubChem (46 vs. 1715 median candidates). Application of MINEs to LC-MS accurate mass data enabled the identity of an unknown peak to be confidently predicted. MINE databases are freely accessible for non-commercial use via user-friendly web-tools at http://minedatabase.mcs.anl.gov and developer-friendly APIs. MINEs improve metabolomics peak identification as compared to general chemical databases whose results include irrelevant synthetic compounds. Furthermore, MINEs complement and expand on previous in silico generated compound databases that focus on human metabolism. We are actively developing the database; future versions of this resource will incorporate transformation rules for spontaneous chemical reactions and more advanced filtering and prioritization of candidate structures. Graphical abstractMINE database construction and access methods. The process of constructing a MINE database from the curated source databases is depicted on the left. The methods for accessing the database are shown on the right.

Jackson Mills and Mine Falls Dams, Nashua, New Hampshire. Reconnaissance Report, Hydroelectric Feasibility. Volume 2. Mine Falls Dam.

DTIC Science & Technology

1980-01-01

producers under a state law of 1978. Until the regulations under PURPA Title II (the National Energy Act of 1978) are promulgated and the PUC reviews this...hour (rWi); end it is FURTr.R ORDERMD, that the Corumission will re-examine th4 PURPA issues in this proceedirg upon the issuance of rules by the F-RC

Mining and Exploitation of Rare Earth Elements in Africa as an Engagement Strategy in US Africa Command

DTIC Science & Technology

2011-06-17

rechargeable batteries, cell phones, catalytic converters, fluorescent lights, hybrid vehicle batteries, and other pollution control devices.21 Figure...79 Lee Yong-tim, “South China Villagers Slam Pollution from Rare Earth Mine,” February 22, 2008, http://www.rfa.org/english...writing and implementing new environmental standards. “The rules will limit pollutants allowed in waste water and emissions of radioactive elements

[Exploring the clinical characters of Shugan Jieyu capsule through text mining].

PubMed

Pu, Zheng-Ping; Xia, Jiang-Ming; Xie, Wei; He, Jin-Cai

2017-09-01

The study was main to explore the clinical characters of Shugan Jieyu capsule through text mining. The data sets of Shugan Jieyu capsule were downloaded from CMCC database by the method of literature retrieved from May 2009 to Jan 2016. Rules of Chinese medical patterns, diseases, symptoms and combination treatment were mined out by data slicing algorithm, and they were demonstrated in frequency tables and two dimension based network. Then totally 190 literature were recruited. The outcomess suggested that SC was most frequently correlated with liver Qi stagnation. Primary depression, depression due to brain disease, concomitant depression followed by physical diseases, concomitant depression followed by schizophrenia and functional dyspepsia were main diseases treated by Shugan Jieyu capsule. Symptoms like low mood, psychic anxiety, somatic anxiety and dysfunction of automatic nerve were mainy relieved bv Shugan Jieyu capsule.For combination treatment. Shugan Jieyu capsule was most commonly used with paroxetine, sertraline and fluoxetine. The research suggested that syndrome types and mining results of Shugan Jieyu capsule were almost the same as its instructions. Syndrome of malnutrition of heart spirit was the potential Chinese medical pattern of Shugan Jieyu capsule. Primary comorbid anxiety and depression, concomitant comorbid anxiety and depression followed by physical diseases, and postpartum depression were potential diseases treated by Shugan Jieyu capsule.For combination treatment, Shugan Jieyu capsule was most commonly used with paroxetine, sertraline and fluoxetine. Copyright© by the Chinese Pharmaceutical Association.

On-Demand Associative Cross-Language Information Retrieval

NASA Astrophysics Data System (ADS)

Geraldo, André Pinto; Moreira, Viviane P.; Gonçalves, Marcos A.

This paper proposes the use of algorithms for mining association rules as an approach for Cross-Language Information Retrieval. These algorithms have been widely used to analyse market basket data. The idea is to map the problem of finding associations between sales items to the problem of finding term translations over a parallel corpus. The proposal was validated by means of experiments using queries in two distinct languages: Portuguese and Finnish to retrieve documents in English. The results show that the performance of our proposed approach is comparable to the performance of the monolingual baseline and to query translation via machine translation, even though these systems employ more complex Natural Language Processing techniques. The combination between machine translation and our approach yielded the best results, even outperforming the monolingual baseline.

A sharable cloud-based pancreaticoduodenectomy collaborative database for physicians: emphasis on security and clinical rule supporting.

PubMed

Yu, Hwan-Jeu; Lai, Hong-Shiee; Chen, Kuo-Hsin; Chou, Hsien-Cheng; Wu, Jin-Ming; Dorjgochoo, Sarangerel; Mendjargal, Adilsaikhan; Altangerel, Erdenebaatar; Tien, Yu-Wen; Hsueh, Chih-Wen; Lai, Feipei

2013-08-01

Pancreaticoduodenectomy (PD) is a major operation with high complication rate. Thereafter, patients may develop morbidity because of the complex reconstruction and loss of pancreatic parenchyma. A well-designed database is very important to address both the short-term and long-term outcomes after PD. The objective of this research was to build an international PD database implemented with security and clinical rule supporting functions, which made the data-sharing easier and improve the accuracy of data. The proposed system is a cloud-based application. To fulfill its requirements, the system comprises four subsystems: a data management subsystem, a clinical rule supporting subsystem, a short message notification subsystem, and an information security subsystem. After completing the surgery, the physicians input the data retrospectively, which are analyzed to study factors associated with post-PD common complications (delayed gastric emptying and pancreatic fistula) to validate the clinical value of this system. Currently, this database contains data from nearly 500 subjects. Five medical centers in Taiwan and two cancer centers in Mongolia are participating in this study. A data mining model of the decision tree analysis showed that elderly patients (>76 years) with pylorus-preserving PD (PPPD) have higher proportion of delayed gastric emptying. About the pancreatic fistula, the data mining model of the decision tree analysis revealed that cases with non-pancreaticogastrostomy (PG) reconstruction - body mass index (BMI)>29.65 or PG reconstruction - BMI>23.7 - non-classic PD have higher proportion of pancreatic fistula after PD. The proposed system allows medical staff to collect and store clinical data in a cloud, sharing the data with other physicians in a secure manner to achieve collaboration in research. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Ontological Modeling of Transformation in Heart Defect Diagrams

PubMed Central

Viswanath, Venkatesh; Tong, Tuanjie; Dinakarpandian, Deendayal; Lee, Yugyung

2006-01-01

The accurate portrayal of a large volume data of variable heart defects is crucial to providing good patient care in pediatric cardiology. Our research aims to span the universe of congenital heart defects by generating illustrative diagrams that enhance data interpretation. To accommodate the range and severity of defects to be represented, we base our diagrams on transformation models applied to a normal heart rather than a static set of defects. These models are based on a domain-specific ontology, clustering, association rule mining and the use of parametric equations specified in a mathematical programming language. PMID:17238451

Demographic Variables of Corruption in the Chinese Construction Industry: Association Rule Analysis of Conviction Records.

PubMed

Yu, Yao; Martek, Igor; Hosseini, M Reza; Chen, Chuan

2018-05-02

Corruption in the construction industry is a serious problem in China. As such, fighting this corruption has become a priority target of the Chinese government, with the main effort being to discover and prosecute its perpetrators. This study profiles the demographic characteristics of major incidences of corruption in construction. It draws on the database of the 83 complete recorded cases of construction related corruption held by the Chinese National Bureau of Corruption Prevention. Categorical variables were drawn from the database, and 'association rule mining analysis' was used to identify associations between variables as a means of profiling perpetrators. Such profiling may be used as predictors of future incidences of corruption, and consequently to inform policy makers in their fight against corruption. The results signal corruption within the Chinese construction industry to be correlated with age, with incidences rising as managers' approach retirement age. Moreover, a majority of perpetrators operate within government agencies, are department deputies in direct contact with projects, and extort the greatest amounts per case from second tier cities. The relatively lengthy average 6.4-year period before cases come to public attention corroborates the view that current efforts at fighting corruption remain inadequate.

Integrated approach using data mining-based decision tree and object-based image analysis for high-resolution urban mapping of WorldView-2 satellite sensor data

NASA Astrophysics Data System (ADS)

Hamedianfar, Alireza; Shafri, Helmi Zulhaidi Mohd

2016-04-01

This paper integrates decision tree-based data mining (DM) and object-based image analysis (OBIA) to provide a transferable model for the detailed characterization of urban land-cover classes using WorldView-2 (WV-2) satellite images. Many articles have been published on OBIA in recent years based on DM for different applications. However, less attention has been paid to the generation of a transferable model for characterizing detailed urban land cover features. Three subsets of WV-2 images were used in this paper to generate transferable OBIA rule-sets. Many features were explored by using a DM algorithm, which created the classification rules as a decision tree (DT) structure from the first study area. The developed DT algorithm was applied to object-based classifications in the first study area. After this process, we validated the capability and transferability of the classification rules into second and third subsets. Detailed ground truth samples were collected to assess the classification results. The first, second, and third study areas achieved 88%, 85%, and 85% overall accuracies, respectively. Results from the investigation indicate that DM was an efficient method to provide the optimal and transferable classification rules for OBIA, which accelerates the rule-sets creation stage in the OBIA classification domain.

An efficient incremental learning mechanism for tracking concept drift in spam filtering

PubMed Central

Sheu, Jyh-Jian; Chu, Ko-Tsung; Li, Nien-Feng; Lee, Cheng-Chi

2017-01-01

This research manages in-depth analysis on the knowledge about spams and expects to propose an efficient spam filtering method with the ability of adapting to the dynamic environment. We focus on the analysis of email’s header and apply decision tree data mining technique to look for the association rules about spams. Then, we propose an efficient systematic filtering method based on these association rules. Our systematic method has the following major advantages: (1) Checking only the header sections of emails, which is different from those spam filtering methods at present that have to analyze fully the email’s content. Meanwhile, the email filtering accuracy is expected to be enhanced. (2) Regarding the solution to the problem of concept drift, we propose a window-based technique to estimate for the condition of concept drift for each unknown email, which will help our filtering method in recognizing the occurrence of spam. (3) We propose an incremental learning mechanism for our filtering method to strengthen the ability of adapting to the dynamic environment. PMID:28182691

Conservation Beyond Park Boundaries: The Impact of Buffer Zones on Deforestation and Mining Concessions in the Peruvian Amazon.

PubMed

Weisse, Mikaela J; Naughton-Treves, Lisa C

2016-08-01

Many researchers have tested whether protected areas save tropical forest, but generally focus on parks and reserves, management units that have internationally recognized standing and clear objectives. Buffer zones have received considerably less attention because of their ambiguous rules and often informal status. Although buffer zones are frequently dismissed as ineffective, they warrant attention given the need for landscape-level approaches to conservation and their prevalence around the world-in Peru, buffer zones cover >10 % of the country. This study examines the effectiveness of buffer zones in the Peruvian Amazon to (a) prevent deforestation and (b) limit the extent of mining concessions. We employ covariate matching to determine the impact of 13 buffer zones on deforestation and mining concessions from 2007 to 2012. Despite variation between sites, these 13 buffer zones have prevented ~320 km(2) of forest loss within their borders during the study period and ~1739 km(2) of mining concessions, an outcome associated with the special approval process for granting formal concessions in these areas. However, a closer look at the buffer zone around the Tambopata National Reserve reveals the difficulties of controlling illegal and informal activities. According to interviews with NGO employees, government officials, and community leaders, enforcement of conservation is limited by uncertain institutional responsibilities, inadequate budgets, and corruption, although formal and community-based efforts to block illicit mining are on the rise. Landscape-level conservation not only requires clear legal protocol for addressing large-scale, formal extractive activities, but there must also be strategies and coordination to combat illegal activities.

Conservation Beyond Park Boundaries: The Impact of Buffer Zones on Deforestation and Mining Concessions in the Peruvian Amazon

NASA Astrophysics Data System (ADS)

Weisse, Mikaela J.; Naughton-Treves, Lisa C.

2016-08-01

Many researchers have tested whether protected areas save tropical forest, but generally focus on parks and reserves, management units that have internationally recognized standing and clear objectives. Buffer zones have received considerably less attention because of their ambiguous rules and often informal status. Although buffer zones are frequently dismissed as ineffective, they warrant attention given the need for landscape-level approaches to conservation and their prevalence around the world—in Peru, buffer zones cover >10 % of the country. This study examines the effectiveness of buffer zones in the Peruvian Amazon to (a) prevent deforestation and (b) limit the extent of mining concessions. We employ covariate matching to determine the impact of 13 buffer zones on deforestation and mining concessions from 2007 to 2012. Despite variation between sites, these 13 buffer zones have prevented ~320 km2 of forest loss within their borders during the study period and ~1739 km2 of mining concessions, an outcome associated with the special approval process for granting formal concessions in these areas. However, a closer look at the buffer zone around the Tambopata National Reserve reveals the difficulties of controlling illegal and informal activities. According to interviews with NGO employees, government officials, and community leaders, enforcement of conservation is limited by uncertain institutional responsibilities, inadequate budgets, and corruption, although formal and community-based efforts to block illicit mining are on the rise. Landscape-level conservation not only requires clear legal protocol for addressing large-scale, formal extractive activities, but there must also be strategies and coordination to combat illegal activities.

Implementing the Seapower Strategy

DTIC Science & Technology

2008-01-01

between the two ends. Here is an example. When Britannia ruled the waves with a global navy to pro- tect the empire, Sir Julian Corbett specified three...because torpedo boats, submarines, and mines threatened cheap kills.7 Upon the rise of the German High Seas Fleet in the decades before World War I...face swarms of small combatants are being developed with accompanying search and attack systems. We have reawakened to the threats from mines and quiet

76 FR 67637 - West Virginia Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-02

... Surface Mining Reclamation and Enforcement (OSM), Interior. ACTION: Proposed rule with public comment period and opportunity for public hearing on proposed amendment. SUMMARY: We are announcing receipt of a... [[Page 67638

Rule-based statistical data mining agents for an e-commerce application

NASA Astrophysics Data System (ADS)

Qin, Yi; Zhang, Yan-Qing; King, K. N.; Sunderraman, Rajshekhar

2003-03-01

Intelligent data mining techniques have useful e-Business applications. Because an e-Commerce application is related to multiple domains such as statistical analysis, market competition, price comparison, profit improvement and personal preferences, this paper presents a hybrid knowledge-based e-Commerce system fusing intelligent techniques, statistical data mining, and personal information to enhance QoS (Quality of Service) of e-Commerce. A Web-based e-Commerce application software system, eDVD Web Shopping Center, is successfully implemented uisng Java servlets and an Oracle81 database server. Simulation results have shown that the hybrid intelligent e-Commerce system is able to make smart decisions for different customers.

Cart'Eaux: an automatic mapping procedure for wastewater networks using machine learning and data mining

NASA Astrophysics Data System (ADS)

Bailly, J. S.; Delenne, C.; Chahinian, N.; Bringay, S.; Commandré, B.; Chaumont, M.; Derras, M.; Deruelle, L.; Roche, M.; Rodriguez, F.; Subsol, G.; Teisseire, M.

2017-12-01

In France, local government institutions must establish a detailed description of wastewater networks. The information should be available, but it remains fragmented (different formats held by different stakeholders) and incomplete. In the "Cart'Eaux" project, a multidisciplinary team, including an industrial partner, develops a global methodology using Machine Learning and Data Mining approaches applied to various types of large data to recover information in the aim of mapping urban sewage systems for hydraulic modelling. Deep-learning is first applied using a Convolution Neural Network to localize manhole covers on 5 cm resolution aerial RGB images. The detected manhole covers are then automatically connected using a tree-shaped graph constrained by industry rules. Based on a Delaunay triangulation, connections are chosen to minimize a cost function depending on pipe length, slope and possible intersection with roads or buildings. A stochastic version of this algorithm is currently being developed to account for positional uncertainty and detection errors, and generate sets of probable networks. As more information is required for hydraulic modeling (slopes, diameters, materials, etc.), text data mining is used to extract network characteristics from data posted on the Web or available through governmental or specific databases. Using an appropriate list of keywords, the web is scoured for documents which are saved in text format. The thematic entities are identified and linked to the surrounding spatial and temporal entities. The methodology is developed and tested on two towns in southern France. The primary results are encouraging: 54% of manhole covers are detected with few false detections, enabling the reconstruction of probable networks. The data mining results are still being investigated. It is clear at this stage that getting numerical values on specific pipes will be challenging. Thus, when no information is found, decision rules will be used to assign admissible numerical values to enable the final hydraulic modelling. Consequently, sensitivity analysis of the hydraulic model will be performed to take into account the uncertainty associated with each piece of information. Project funded by the European Regional Development Fund and the Occitanie Region.

Beyond nutrient-based food indices: a data mining approach to search for a quantitative holistic index reflecting the degree of food processing and including physicochemical properties.

PubMed

Fardet, Anthony; Lakhssassi, Sanaé; Briffaz, Aurélien

2018-01-24

Processing has major impacts on both the structure and composition of food and hence on nutritional value. In particular, high consumption of ultra-processed foods (UPFs) is associated with increased risks of obesity and diabetes. Unfortunately, existing food indices only focus on food nutritional content while failing to consider either food structure or the degree of processing. The objectives of this study were thus to link non-nutrient food characteristics (texture, water activity (a w ), glycemic and satiety potentials (FF), and shelf life) to the degree of processing; search for associations between these characteristics with nutritional composition; search for a holistic quantitative technological index; and determine quantitative rules for a food to be defined as UPF using data mining. Among the 280 most widely consumed foods by the elderly in France, 139 solid/semi-solid foods were selected for textural and a w measurements, and classified according to three degrees of processing. Our results showed that minimally-processed foods were less hyperglycemic, more satiating, had better nutrient profile, higher a w , shorter shelf life, lower maximum stress, and higher energy at break than UPFs. Based on 72 food variables, multivariate analyses differentiated foods according to their degree of processing. Then technological indices including food nutritional composition, a w , FF and textural parameters were tested against technological groups. Finally, a LIM score (nutrients to limit) ≥8 per 100 kcal and a number of ingredients/additives >4 are relevant, but not sufficient, rules to define UPFs. We therefore suggest that food health potential should be first defined by its degree of processing.

Biclustering Learning of Trading Rules.

PubMed

Huang, Qinghua; Wang, Ting; Tao, Dacheng; Li, Xuelong

2015-10-01

Technical analysis with numerous indicators and patterns has been regarded as important evidence for making trading decisions in financial markets. However, it is extremely difficult for investors to find useful trading rules based on numerous technical indicators. This paper innovatively proposes the use of biclustering mining to discover effective technical trading patterns that contain a combination of indicators from historical financial data series. This is the first attempt to use biclustering algorithm on trading data. The mined patterns are regarded as trading rules and can be classified as three trading actions (i.e., the buy, the sell, and no-action signals) with respect to the maximum support. A modified K nearest neighborhood ( K -NN) method is applied to classification of trading days in the testing period. The proposed method [called biclustering algorithm and the K nearest neighbor (BIC- K -NN)] was implemented on four historical datasets and the average performance was compared with the conventional buy-and-hold strategy and three previously reported intelligent trading systems. Experimental results demonstrate that the proposed trading system outperforms its counterparts and will be useful for investment in various financial markets.

Using fuzzy data mining to diagnose patients' degrees of melancholia

NASA Astrophysics Data System (ADS)

Huang, Yo-Ping; Kuo, Wen-Lin

2011-06-01

The common treatments of melancholia are psychotherapy and taking medicines. The psychotherapy treatment which this study focuses on is limited by time and location. It is easier for psychiatrists to grasp information from clinical manifestation but it is difficult for psychiatrists to collect information from patients' daily conversations or emotion. To design a system which psychiatrists enable to capture patients' daily symptoms will show great help in the treatment. This study proposes to use fuzzy data mining algorithm to find association rules among keywords segmented from patients' daily voice/text messages to assist psychiatrists extract useful information before outpatient service. Patients of melancholia can use devices such as mobile phones or computers to record their own emotion anytime and anywhere and then uploading the recorded files to the back-end server for further analysis. The analytical results can be used for psychiatrists to diagnose patients' degrees of melancholia. Experimental results will be given to verify the effectiveness of the proposed methodology.

Development of a GIService based on spatial data mining for location choice of convenience stores in Taipei City

NASA Astrophysics Data System (ADS)

Jung, Chinte; Sun, Chih-Hong

2006-10-01

Motivated by the increasing accessibility of technology, more and more spatial data are being made digitally available. How to extract the valuable knowledge from these large (spatial) databases is becoming increasingly important to businesses, as well. It is essential to be able to analyze and utilize these large datasets, convert them into useful knowledge, and transmit them through GIS-enabled instruments and the Internet, conveying the key information to business decision-makers effectively and benefiting business entities. In this research, we combine the techniques of GIS, spatial decision support system (SDSS), spatial data mining (SDM), and ArcGIS Server to achieve the following goals: (1) integrate databases from spatial and non-spatial datasets about the locations of businesses in Taipei, Taiwan; (2) use the association rules, one of the SDM methods, to extract the knowledge from the integrated databases; and (3) develop a Web-based SDSS GIService as a location-selection tool for business by the product of ArcGIS Server.

Entity Bases: Large-Scale Knowledgebases for Intelligence Data

DTIC Science & Technology

2009-02-01

declaratively expressed as Datalog rules . The EntityBase supports two query scenarios: • Free-Form Querying: A human analyst or a client program can pose...integration, Prometheus follows the Inverse Rules algo- rithm (Duschka 1997) with additional optimizations (Thakkar et al. 2005). We use the mediator...Discovery and Data Mining (PAKDD󈧈), Sydney, Australia. Crammer , K., Dekel, O., Keshet, J., Shalev-Shwartz, S., and Singer, Y. (2006). Online passive

Analyzing Divisia Rules Extracted from a Feedforward Neural Network

DTIC Science & Technology

2006-03-01

assumptions. (Barnett and work, Data Mining, Rule Generation Serletis give a detailed treatment of the the- ory of monetary aggregation [1].) However, 1... Serletis , A. (Eds.) (2000), The The- Swizerland, 1995. ory of Monetary Aggregation, North-H ollandeAmsterdam, Chgaptero , pp.- [11] Vincent A. Schmidt and...gas, Nevada, 2002. sets. Macroeconomic Dynamics, 1:485-512, 1997. Reprinted in Barnett, WA. [12] Vincent A. Schmidt and Jane M. Binner. and Serletis

[Rule of Clinical Application of Auricular Acupuncture Based on Data Mining].

PubMed

Bao, Na; Wang, Qiong; Sun, Yan-Hui; Shi, Jing; Li, Xiao-Feng; Xu, Jing; Xing, Hai-Jiao; Zhang, Xuan-Ping; Zhang, Xin; Du, Yu-Zhu; Li, Jun-Lei; Yang, Qing-Qing; Feng, Xin-Xin; Jia, Chun-Sheng; Wang, Jian-Ling

2017-02-25

To explore the rule of clinical application of auricular acupuncture therapy by data mining in order to guide clinical practice. The data base about single auricular acupuncture therapy for different clinical diseases was established by collection, sorting, screening, recording, collation, data extraction, statistic analysis on data samples from journals, academic theses dissertations published in near 60 years. The application rules of auricular therapy including its predominant diseases, stimulus modality, therapeutic effect, and angle of needling were summarized by data mining technique. Auricular acupuncture therapy has been widely and mostly used in the internal medicine department, accounting for 48.56%. Of stimulus modalities, auricular point paste and pressure is applied with the highest frequency, accounting for 64%. The highest effective rate is found in the surgery department diseases(81.41%). Pressure is the most effective stimulus in the internal medi-cine department, and bloodletting combined with paste and pressure in the surgery department, auricular point injection in the gynecology and pediatrics departments, bloodletting in the ophthalmology and otorhinolaryngology department, and auricular point incision in the dermatology department. Auricular point injection has remarkable effect. Bloodletting combined with paste and pressure has nearly the same effect as bloodletting in the same medical department except dematology department. Otherwise, angle of needling is rarely studied. Auricular therapy is widely used and has remarkable effect in treating diseases by using different stimulus modalities. Whereas the angle of needling is rarely studied and future investigation is needed.

Implementation of hospital examination reservation system using data mining technique.

PubMed

Cha, Hyo Soung; Yoon, Tae Sik; Ryu, Ki Chung; Shin, Il Won; Choe, Yang Hyo; Lee, Kyoung Yong; Lee, Jae Dong; Ryu, Keun Ho; Chung, Seung Hyun

2015-04-01

New methods for obtaining appropriate information for users have been attempted with the development of information technology and the Internet. Among such methods, the demand for systems and services that can improve patient satisfaction has increased in hospital care environments. In this paper, we proposed the Hospital Exam Reservation System (HERS), which uses the data mining method. First, we focused on carrying clinical exam data and finding the optimal schedule for generating rules using the multi-examination pattern-mining algorithm. Then, HERS was applied by a rule master and recommending system with an exam log. Finally, HERS was designed as a user-friendly interface. HERS has been applied at the National Cancer Center in Korea since June 2014. As the number of scheduled exams increased, the time required to schedule more than a single condition decreased (from 398.67% to 168.67% and from 448.49% to 188.49%; p < 0.0001). As the number of tests increased, the difference between HERS and non-HERS increased (from 0.18 days to 0.81 days). It was possible to expand the efficiency of HERS studies using mining technology in not only exam reservations, but also the medical environment. The proposed system based on doctor prescription removes exams that were not executed in order to improve recommendation accuracy. In addition, we expect HERS to become an effective system in various medical environments.

Classification Based on Pruning and Double Covered Rule Sets for the Internet of Things Applications

PubMed Central

Zhou, Zhongmei; Wang, Weiping

2014-01-01

The Internet of things (IOT) is a hot issue in recent years. It accumulates large amounts of data by IOT users, which is a great challenge to mining useful knowledge from IOT. Classification is an effective strategy which can predict the need of users in IOT. However, many traditional rule-based classifiers cannot guarantee that all instances can be covered by at least two classification rules. Thus, these algorithms cannot achieve high accuracy in some datasets. In this paper, we propose a new rule-based classification, CDCR-P (Classification based on the Pruning and Double Covered Rule sets). CDCR-P can induce two different rule sets A and B. Every instance in training set can be covered by at least one rule not only in rule set A, but also in rule set B. In order to improve the quality of rule set B, we take measure to prune the length of rules in rule set B. Our experimental results indicate that, CDCR-P not only is feasible, but also it can achieve high accuracy. PMID:24511304

Classification based on pruning and double covered rule sets for the internet of things applications.

PubMed

Li, Shasha; Zhou, Zhongmei; Wang, Weiping

2014-01-01

The Internet of things (IOT) is a hot issue in recent years. It accumulates large amounts of data by IOT users, which is a great challenge to mining useful knowledge from IOT. Classification is an effective strategy which can predict the need of users in IOT. However, many traditional rule-based classifiers cannot guarantee that all instances can be covered by at least two classification rules. Thus, these algorithms cannot achieve high accuracy in some datasets. In this paper, we propose a new rule-based classification, CDCR-P (Classification based on the Pruning and Double Covered Rule sets). CDCR-P can induce two different rule sets A and B. Every instance in training set can be covered by at least one rule not only in rule set A, but also in rule set B. In order to improve the quality of rule set B, we take measure to prune the length of rules in rule set B. Our experimental results indicate that, CDCR-P not only is feasible, but also it can achieve high accuracy.

Discovering Sentinel Rules for Business Intelligence

NASA Astrophysics Data System (ADS)

Middelfart, Morten; Pedersen, Torben Bach

This paper proposes the concept of sentinel rules for multi-dimensional data that warns users when measure data concerning the external environment changes. For instance, a surge in negative blogging about a company could trigger a sentinel rule warning that revenue will decrease within two months, so a new course of action can be taken. Hereby, we expand the window of opportunity for organizations and facilitate successful navigation even though the world behaves chaotically. Since sentinel rules are at the schema level as opposed to the data level, and operate on data changes as opposed to absolute data values, we are able to discover strong and useful sentinel rules that would otherwise be hidden when using sequential pattern mining or correlation techniques. We present a method for sentinel rule discovery and an implementation of this method that scales linearly on large data volumes.

The new physician as unwitting quantum mechanic: is adapting Dirac's inference system best practice for personalized medicine, genomics, and proteomics?

PubMed

Robson, Barry

2007-08-01

What is the Best Practice for automated inference in Medical Decision Support for personalized medicine? A known system already exists as Dirac's inference system from quantum mechanics (QM) using bra-kets and bras where A and B are states, events, or measurements representing, say, clinical and biomedical rules. Dirac's system should theoretically be the universal best practice for all inference, though QM is notorious as sometimes leading to bizarre conclusions that appear not to be applicable to the macroscopic world of everyday world human experience and medical practice. It is here argued that this apparent difficulty vanishes if QM is assigned one new multiplication function @, which conserves conditionality appropriately, making QM applicable to classical inference including a quantitative form of the predicate calculus. An alternative interpretation with the same consequences is if every i = radical-1 in Dirac's QM is replaced by h, an entity distinct from 1 and i and arguably a hidden root of 1 such that h2 = 1. With that exception, this paper is thus primarily a review of the application of Dirac's system, by application of linear algebra in the complex domain to help manipulate information about associations and ontology in complicated data. Any combined bra-ket can be shown to be composed only of the sum of QM-like bra and ket weights c(), times an exponential function of Fano's mutual information measure I(A; B) about the association between A and B, that is, an association rule from data mining. With the weights and Fano measure re-expressed as expectations on finite data using Riemann's Incomplete (i.e., Generalized) Zeta Functions, actual counts of observations for real world sparse data can be readily utilized. Finally, the paper compares identical character, distinguishability of states events or measurements, correlation, mutual information, and orthogonal character, important issues in data mining and biomedical analytics, as in QM.

A fuzzy classifier system for process control

NASA Technical Reports Server (NTRS)

Karr, C. L.; Phillips, J. C.

1994-01-01

A fuzzy classifier system that discovers rules for controlling a mathematical model of a pH titration system was developed by researchers at the U.S. Bureau of Mines (USBM). Fuzzy classifier systems successfully combine the strengths of learning classifier systems and fuzzy logic controllers. Learning classifier systems resemble familiar production rule-based systems, but they represent their IF-THEN rules by strings of characters rather than in the traditional linguistic terms. Fuzzy logic is a tool that allows for the incorporation of abstract concepts into rule based-systems, thereby allowing the rules to resemble the familiar 'rules-of-thumb' commonly used by humans when solving difficult process control and reasoning problems. Like learning classifier systems, fuzzy classifier systems employ a genetic algorithm to explore and sample new rules for manipulating the problem environment. Like fuzzy logic controllers, fuzzy classifier systems encapsulate knowledge in the form of production rules. The results presented in this paper demonstrate the ability of fuzzy classifier systems to generate a fuzzy logic-based process control system.

Managing the Big Data Avalanche in Astronomy - Data Mining the Galaxy Zoo Classification Database

NASA Astrophysics Data System (ADS)

Borne, Kirk D.

2014-01-01

We will summarize a variety of data mining experiments that have been applied to the Galaxy Zoo database of galaxy classifications, which were provided by the volunteer citizen scientists. The goal of these exercises is to learn new and improved classification rules for diverse populations of galaxies, which can then be applied to much larger sky surveys of the future, such as the LSST (Large Synoptic Sky Survey), which is proposed to obtain detailed photometric data for approximately 20 billion galaxies. The massive Big Data that astronomy projects will generate in the future demand greater application of data mining and data science algorithms, as well as greater training of astronomy students in the skills of data mining and data science. The project described here has involved several graduate and undergraduate research assistants at George Mason University.

Ion Channel ElectroPhysiology Ontology (ICEPO) - a case study of text mining assisted ontology development.

PubMed

Elayavilli, Ravikumar Komandur; Liu, Hongfang

2016-01-01

Computational modeling of biological cascades is of great interest to quantitative biologists. Biomedical text has been a rich source for quantitative information. Gathering quantitative parameters and values from biomedical text is one significant challenge in the early steps of computational modeling as it involves huge manual effort. While automatically extracting such quantitative information from bio-medical text may offer some relief, lack of ontological representation for a subdomain serves as impedance in normalizing textual extractions to a standard representation. This may render textual extractions less meaningful to the domain experts. In this work, we propose a rule-based approach to automatically extract relations involving quantitative data from biomedical text describing ion channel electrophysiology. We further translated the quantitative assertions extracted through text mining to a formal representation that may help in constructing ontology for ion channel events using a rule based approach. We have developed Ion Channel ElectroPhysiology Ontology (ICEPO) by integrating the information represented in closely related ontologies such as, Cell Physiology Ontology (CPO), and Cardiac Electro Physiology Ontology (CPEO) and the knowledge provided by domain experts. The rule-based system achieved an overall F-measure of 68.93% in extracting the quantitative data assertions system on an independently annotated blind data set. We further made an initial attempt in formalizing the quantitative data assertions extracted from the biomedical text into a formal representation that offers potential to facilitate the integration of text mining into ontological workflow, a novel aspect of this study. This work is a case study where we created a platform that provides formal interaction between ontology development and text mining. We have achieved partial success in extracting quantitative assertions from the biomedical text and formalizing them in ontological framework. The ICEPO ontology is available for download at http://openbionlp.org/mutd/supplementarydata/ICEPO/ICEPO.owl.

Fact Sheet - Final Air Toxics Rule for Gold Mine Ore Processing and Production

EPA Pesticide Factsheets

Fact sheet summarizing main points of National Emissions Standards for Hazardous Air Pollutants for gold ore processing and production facilities, the seventh largest source of mercury air emission in the United States.

Chromite Ore from the Transvaal Region of South Africa

EPA Pesticide Factsheets

In 2001, EPA finalized a rule to to delete both chromite ore mined in the Transvaal Region of South Africa and the unreacted ore component of the chromite ore processing residue (COPR) from TRI reporting requirements.

78 FR 77024 - Telemarketing Sales Rule; Notice of Termination of Caller ID Rulemaking

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-20

..., data mining and anomaly detection, and call-blocking technology). \\19\\ AT&T Servs., Inc., No. 00040, at... technically feasible, by looking at the signaling data . . . to distinguish between a CPN [calling party...

An intelligent knowledge mining model for kidney cancer using rough set theory.

PubMed

Durai, M A Saleem; Acharjya, D P; Kannan, A; Iyengar, N Ch Sriman Narayana

2012-01-01

Medical diagnosis processes vary in the degree to which they attempt to deal with different complicating aspects of diagnosis such as relative importance of symptoms, varied symptom pattern and the relation between diseases themselves. Rough set approach has two major advantages over the other methods. First, it can handle different types of data such as categorical, numerical etc. Secondly, it does not make any assumption like probability distribution function in stochastic modeling or membership grade function in fuzzy set theory. It involves pattern recognition through logical computational rules rather than approximating them through smooth mathematical functional forms. In this paper we use rough set theory as a data mining tool to derive useful patterns and rules for kidney cancer faulty diagnosis. In particular, the historical data of twenty five research hospitals and medical college is used for validation and the results show the practical viability of the proposed approach.

The expert explorer: a tool for hospital data visualization and adverse drug event rules validation.

PubMed

Băceanu, Adrian; Atasiei, Ionuţ; Chazard, Emmanuel; Leroy, Nicolas

2009-01-01

An important part of adverse drug events (ADEs) detection is the validation of the clinical cases and the assessment of the decision rules to detect ADEs. For that purpose, a software called "Expert Explorer" has been designed by Ideea Advertising. Anonymized datasets have been extracted from hospitals into a common repository. The tool has 3 main features. (1) It can display hospital stays in a visual and comprehensive way (diagnoses, drugs, lab results, etc.) using tables and pretty charts. (2) It allows designing and executing dashboards in order to generate knowledge about ADEs. (3) It finally allows uploading decision rules obtained from data mining. Experts can then review the rules, the hospital stays that match the rules, and finally give their advice thanks to specialized forms. Then the rules can be validated, invalidated, or improved (knowledge elicitation phase).

Phylogenetic Tree Analysis of the Cold-Hot Nature of Traditional Chinese Marine Medicine for Possible Anticancer Activity

PubMed Central

Song, Xuxia; Li, Xuebo; Zhang, Fengcong; Wang, Changyun

2017-01-01

Traditional Chinese Marine Medicine (TCMM) represents one of the medicinal resources for research and development of novel anticancer drugs. In this study, to investigate the presence of anticancer activity (AA) displayed by cold or hot nature of TCMM, we analyzed the association relationship and the distribution regularity of TCMMs with different nature (613 TCMMs originated from 1,091 species of marine organisms) via association rules mining and phylogenetic tree analysis. The screened association rules were collected from three taxonomy groups: (1) Bacteria superkingdom, Phaeophyceae class, Fucales order, Sargassaceae family, and Sargassum genus; (2) Viridiplantae kingdom, Streptophyta phylum, Malpighiales class, and Rhizophoraceae family; (3) Holothuroidea class, Aspidochirotida order, and Holothuria genus. Our analyses showed that TCMMs with closer taxonomic relationship were more likely to possess anticancer bioactivity. We found that the cluster pattern of marine organisms with reported AA tended to cluster with cold nature TCMMs. Moreover, TCMMs with salty-cold nature demonstrated properties for softening hard mass and removing stasis to treat cancers, and species within Metazoa or Viridiplantae kingdom of cold nature were more likely to contain AA properties. We propose that TCMMs from these marine groups may enable focused bioprospecting for discovery of novel anticancer drugs derived from marine bioresources. PMID:28191021

Analyzing injury severity factors at highway railway grade crossing accidents involving vulnerable road users: A comparative study.

PubMed

Ghomi, Haniyeh; Bagheri, Morteza; Fu, Liping; Miranda-Moreno, Luis F

2016-11-16

The main objective of this study is to identify the main factors associated with injury severity of vulnerable road users (VRUs) involved in accidents at highway railroad grade crossings (HRGCs) using data mining techniques. This article applies an ordered probit model, association rules, and classification and regression tree (CART) algorithms to the U.S. Federal Railroad Administration's (FRA) HRGC accident database for the period 2007-2013 to identify VRU injury severity factors at HRGCs. The results show that train speed is a key factor influencing injury severity. Further analysis illustrated that the presence of illumination does not reduce the severity of accidents for high-speed trains. In addition, there is a greater propensity toward fatal accidents for elderly road users compared to younger individuals. Interestingly, at night, injury accidents involving female road users are more severe compared to those involving males. The ordered probit model was the primary technique, and CART and association rules act as the supporter and identifier of interactions between variables. All 3 algorithms' results consistently show that the most influential accident factors are train speed, VRU age, and gender. The findings of this research could be applied for identifying high-risk hotspots and developing cost-effective countermeasures targeting VRUs at HRGCs.

Statistical methods of estimating mining costs

USGS Publications Warehouse

Long, K.R.

2011-01-01

Until it was defunded in 1995, the U.S. Bureau of Mines maintained a Cost Estimating System (CES) for prefeasibility-type economic evaluations of mineral deposits and estimating costs at producing and non-producing mines. This system had a significant role in mineral resource assessments to estimate costs of developing and operating known mineral deposits and predicted undiscovered deposits. For legal reasons, the U.S. Geological Survey cannot update and maintain CES. Instead, statistical tools are under development to estimate mining costs from basic properties of mineral deposits such as tonnage, grade, mineralogy, depth, strip ratio, distance from infrastructure, rock strength, and work index. The first step was to reestimate "Taylor's Rule" which relates operating rate to available ore tonnage. The second step was to estimate statistical models of capital and operating costs for open pit porphyry copper mines with flotation concentrators. For a sample of 27 proposed porphyry copper projects, capital costs can be estimated from three variables: mineral processing rate, strip ratio, and distance from nearest railroad before mine construction began. Of all the variables tested, operating costs were found to be significantly correlated only with strip ratio.

An Automated Technique to Construct a Knowledge Base of Traditional Chinese Herbal Medicine for Cancers: An Exploratory Study for Breast Cancer.

PubMed

Nguyen, Phung Anh; Yang, Hsuan-Chia; Xu, Rong; Li, Yu-Chuan Jack

2018-01-01

Traditional Chinese Medicine utilization has rapidly increased worldwide. However, there is limited database provides the information of TCM herbs and diseases. The study aims to identify and evaluate the meaningful associations between TCM herbs and breast cancer by using the association rule mining (ARM) techniques. We employed the ARM techniques for 19.9 million TCM prescriptions by using Taiwan National Health Insurance claim database from 1999 to 2013. 364 TCM herbs-breast cancer associations were derived from those prescriptions and were then filtered by their support of 20. Resulting of 296 associations were evaluated by comparing to a gold-standard that was curated information from Chinese-Wikipedia with the following terms, cancer, tumor, malignant. All 14 TCM herbs-breast cancer associations with their confidence of 1% were valid when compared to gold-standard. For other confidences, the statistical results showed consistently with high precisions. We thus succeed to identify the TCM herbs-breast cancer associations with useful techniques.

Ensuring the Environmental and Industrial Safety in Solid Mineral Deposit Surface Mining

NASA Astrophysics Data System (ADS)

Trubetskoy, Kliment; Rylnikova, Marina; Esina, Ekaterina

2017-11-01

The growing environmental pressure of mineral deposit surface mining and severization of industrial safety requirements dictate the necessity of refining the regulatory framework governing safe and efficient development of underground resources. The applicable regulatory documentation governing the procedure of ore open-pit wall and bench stability design for the stage of pit reaching its final boundary was issued several decades ago. Over recent decades, mining and geomechanical conditions have changed significantly in surface mining operations, numerous new software packages and computer developments have appeared, opportunities of experimental methods of source data collection and processing, grounding of the permissible parameters of open pit walls have changed dramatically, and, thus, methods of risk assessment have been perfected [10-13]. IPKON RAS, with the support of the Federal Service for Environmental Supervision, assumed the role of the initiator of the project for the development of Federal norms and regulations of industrial safety "Rules for ensuring the stability of walls and benches of open pits, open-cast mines and spoil banks", which contribute to the improvement of economic efficiency and safety of mineral deposit surface mining and enhancement of the competitiveness of Russian mines at the international level that is very important in the current situation.

Knowledge discovery with classification rules in a cardiovascular dataset.

PubMed

Podgorelec, Vili; Kokol, Peter; Stiglic, Milojka Molan; Hericko, Marjan; Rozman, Ivan

2005-12-01

In this paper we study an evolutionary machine learning approach to data mining and knowledge discovery based on the induction of classification rules. A method for automatic rules induction called AREX using evolutionary induction of decision trees and automatic programming is introduced. The proposed algorithm is applied to a cardiovascular dataset consisting of different groups of attributes which should possibly reveal the presence of some specific cardiovascular problems in young patients. A case study is presented that shows the use of AREX for the classification of patients and for discovering possible new medical knowledge from the dataset. The defined knowledge discovery loop comprises a medical expert's assessment of induced rules to drive the evolution of rule sets towards more appropriate solutions. The final result is the discovery of a possible new medical knowledge in the field of pediatric cardiology.

20 CFR 410.687 - Rules governing the representation and advising of claimants and parties.

Code of Federal Regulations, 2011 CFR

2011-04-01

... ADMINISTRATION FEDERAL COAL MINE HEALTH AND SAFETY ACT OF 1969, TITLE IV-BLACK LUNG BENEFITS (1969... attorney or other representative shall: (a) With intent to defraud, in any matter willfully and knowingly...

Associations between socio-demographic characteristics and chemical concentrations contributing to cumulative exposures in the United States.

PubMed

Huang, Hongtai; Tornero-Velez, Rogelio; Barzyk, Timothy M

2017-11-01

Association rule mining (ARM) has been widely used to identify associations between various entities in many fields. Although some studies have utilized it to analyze the relationship between chemicals and human health effects, fewer have used this technique to identify and quantify associations between environmental and social stressors. Socio-demographic variables were generated based on U.S. Census tract-level income, race/ethnicity population percentage, education level, and age information from the 2010-2014, 5-Year Summary files in the American Community Survey (ACS) database, and chemical variables were generated by utilizing the 2011 National-Scale Air Toxics Assessment (NATA) census tract-level air pollutant exposure concentration data. Six mobile- and industrial-source pollutants were chosen for analysis, including acetaldehyde, benzene, cyanide, particulate matter components of diesel engine emissions (namely, diesel PM), toluene, and 1,3-butadiene. ARM was then applied to quantify and visualize the associations between the chemical and socio-demographic variables. Census tracts with a high percentage of racial/ethnic minorities and populations with low income tended to have higher estimated chemical exposure concentrations (fourth quartile), especially for diesel PM, 1,3-butadiene, and toluene. In contrast, census tracts with an average population age of 40-50 years, a low percentage of racial/ethnic minorities, and moderate-income levels were more likely to have lower estimated chemical exposure concentrations (first quartile). Unsupervised data mining methods can be used to evaluate potential associations between environmental inequalities and social disparities, while providing support in public health decision-making contexts.

Novel approach for identification of influenza virus host range and zoonotic transmissible sequences by determination of host-related associative positions in viral genome segments.

PubMed

Kargarfard, Fatemeh; Sami, Ashkan; Mohammadi-Dehcheshmeh, Manijeh; Ebrahimie, Esmaeil

2016-11-16

Recent (2013 and 2009) zoonotic transmission of avian or porcine influenza to humans highlights an increase in host range by evading species barriers. Gene reassortment or antigenic shift between viruses from two or more hosts can generate a new life-threatening virus when the new shuffled virus is no longer recognized by antibodies existing within human populations. There is no large scale study to help understand the underlying mechanisms of host transmission. Furthermore, there is no clear understanding of how different segments of the influenza genome contribute in the final determination of host range. To obtain insight into the rules underpinning host range determination, various supervised machine learning algorithms were employed to mine reassortment changes in different viral segments in a range of hosts. Our multi-host dataset contained whole segments of 674 influenza strains organized into three host categories: avian, human, and swine. Some of the sequences were assigned to multiple hosts. In point of fact, the datasets are a form of multi-labeled dataset and we utilized a multi-label learning method to identify discriminative sequence sites. Then algorithms such as CBA, Ripper, and decision tree were applied to extract informative and descriptive association rules for each viral protein segment. We found informative rules in all segments that are common within the same host class but varied between different hosts. For example, for infection of an avian host, HA14V and NS1230S were the most important discriminative and combinatorial positions. Host range identification is facilitated by high support combined rules in this study. Our major goal was to detect discriminative genomic positions that were able to identify multi host viruses, because such viruses are likely to cause pandemic or disastrous epidemics.

RADSS: an integration of GIS, spatial statistics, and network service for regional data mining

NASA Astrophysics Data System (ADS)

Hu, Haitang; Bao, Shuming; Lin, Hui; Zhu, Qing

2005-10-01

Regional data mining, which aims at the discovery of knowledge about spatial patterns, clusters or association between regions, has widely applications nowadays in social science, such as sociology, economics, epidemiology, crime, and so on. Many applications in the regional or other social sciences are more concerned with the spatial relationship, rather than the precise geographical location. Based on the spatial continuity rule derived from Tobler's first law of geography: observations at two sites tend to be more similar to each other if the sites are close together than if far apart, spatial statistics, as an important means for spatial data mining, allow the users to extract the interesting and useful information like spatial pattern, spatial structure, spatial association, spatial outlier and spatial interaction, from the vast amount of spatial data or non-spatial data. Therefore, by integrating with the spatial statistical methods, the geographical information systems will become more powerful in gaining further insights into the nature of spatial structure of regional system, and help the researchers to be more careful when selecting appropriate models. However, the lack of such tools holds back the application of spatial data analysis techniques and development of new methods and models (e.g., spatio-temporal models). Herein, we make an attempt to develop such an integrated software and apply it into the complex system analysis for the Poyang Lake Basin. This paper presents a framework for integrating GIS, spatial statistics and network service in regional data mining, as well as their implementation. After discussing the spatial statistics methods involved in regional complex system analysis, we introduce RADSS (Regional Analysis and Decision Support System), our new regional data mining tool, by integrating GIS, spatial statistics and network service. RADSS includes the functions of spatial data visualization, exploratory spatial data analysis, and spatial statistics. The tool also includes some fundamental spatial and non-spatial database in regional population and environment, which can be updated by external database via CD or network. Utilizing this data mining and exploratory analytical tool, the users can easily and quickly analyse the huge mount of the interrelated regional data, and better understand the spatial patterns and trends of the regional development, so as to make a credible and scientific decision. Moreover, it can be used as an educational tool for spatial data analysis and environmental studies. In this paper, we also present a case study on Poyang Lake Basin as an application of the tool and spatial data mining in complex environmental studies. At last, several concluding remarks are discussed.

Comparison of rule induction, decision trees and formal concept analysis approaches for classification

NASA Astrophysics Data System (ADS)

Kotelnikov, E. V.; Milov, V. R.

2018-05-01

Rule-based learning algorithms have higher transparency and easiness to interpret in comparison with neural networks and deep learning algorithms. These properties make it possible to effectively use such algorithms to solve descriptive tasks of data mining. The choice of an algorithm depends also on its ability to solve predictive tasks. The article compares the quality of the solution of the problems with binary and multiclass classification based on the experiments with six datasets from the UCI Machine Learning Repository. The authors investigate three algorithms: Ripper (rule induction), C4.5 (decision trees), In-Close (formal concept analysis). The results of the experiments show that In-Close demonstrates the best quality of classification in comparison with Ripper and C4.5, however the latter two generate more compact rule sets.

PISA — Pooling Information from Several Agents: Multiplayer Argumentation from Experience

NASA Astrophysics Data System (ADS)

Wardeh, Maya; Bench-Capon, Trevor; Coenen, Frans

In this paper a framework, PISA (Pooling Information from Several Agents), to facilitate multiplayer (three or more protagonists), "argumentation from experience" is described. Multiplayer argumentation is a form of dialogue game involving three or more players. The PISA framework is founded on a two player argumentation framework, PADUA (Protocol for Argumentation Dialogue Using Association Rules), also developed by the authors. One of the main advantages of both PISA and PADUA is that they avoid the resource intensive need to predefine a knowledge base, instead data mining techniques are used to facilitate the provision of "just in time" information. Many of the issues associated with multiplayer dialogue games do not present a significant challenge in the two player game. The main original contributions of this paper are the mechanisms whereby the PISA framework addresses these challenges.

43 CFR 3481.4 - Temporary interruption in coal severance.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 43 Public Lands: Interior 2 2013-10-01 2013-10-01 false Temporary interruption in coal severance... LAND MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) COAL EXPLORATION AND MINING OPERATIONS RULES General Provisions § 3481.4 Temporary interruption in coal severance. ...

43 CFR 3481.4 - Temporary interruption in coal severance.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 43 Public Lands: Interior 2 2012-10-01 2012-10-01 false Temporary interruption in coal severance... LAND MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) COAL EXPLORATION AND MINING OPERATIONS RULES General Provisions § 3481.4 Temporary interruption in coal severance. ...

43 CFR 3481.4 - Temporary interruption in coal severance.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 43 Public Lands: Interior 2 2014-10-01 2014-10-01 false Temporary interruption in coal severance... LAND MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) COAL EXPLORATION AND MINING OPERATIONS RULES General Provisions § 3481.4 Temporary interruption in coal severance. ...

43 CFR 3481.4 - Temporary interruption in coal severance.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 43 Public Lands: Interior 2 2011-10-01 2011-10-01 false Temporary interruption in coal severance... LAND MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) COAL EXPLORATION AND MINING OPERATIONS RULES General Provisions § 3481.4 Temporary interruption in coal severance. ...

75 FR 61366 - Montana Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-05

... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and opportunity for public hearing on proposed amendment. SUMMARY: We are announcing receipt of a proposed... that we will follow for the public hearing, if one is requested. DATES: We will accept written comments...

78 FR 13004 - Wyoming Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-26

... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and opportunity for public hearing on proposed amendment. SUMMARY: We are announcing receipt of a proposed... will follow for the public hearing, if one is requested. DATES: We will accept written comments on this...

75 FR 81459 - Simplified Proceedings

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-28

... FEDERAL MINE SAFETY AND HEALTH REVIEW COMMISSION 29 CFR Part 2700 Simplified Proceedings AGENCY... Commission is publishing a final rule to simplify the procedures for handling certain civil penalty.... Electronic comments should state ``Comments on Simplified Proceedings'' in the subject line and be sent to...

78 FR 11796 - Kentucky Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-20

... personal identifying information from public review, we cannot guarantee that we will be able to do so... our review of the proposed amendment after the close of the public comment period and determine... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and...

76 FR 73885 - Mandatory Reporting of Greenhouse Gases

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-29

.... 211112 Natural gas liquid extraction facilities. Underground Coal Mines........ 212113 Underground... natural gas liquids in addition to suppliers of petroleum products. 2. Summary of Comments and Responses... Mandatory Reporting of Greenhouse Gases; Final Rule #0;#0;Federal Register / Vol. 76, No. 229 / Tuesday...

Chapter 16: text mining for translational bioinformatics.

PubMed

Cohen, K Bretonnel; Hunter, Lawrence E

2013-04-01

Text mining for translational bioinformatics is a new field with tremendous research potential. It is a subfield of biomedical natural language processing that concerns itself directly with the problem of relating basic biomedical research to clinical practice, and vice versa. Applications of text mining fall both into the category of T1 translational research-translating basic science results into new interventions-and T2 translational research, or translational research for public health. Potential use cases include better phenotyping of research subjects, and pharmacogenomic research. A variety of methods for evaluating text mining applications exist, including corpora, structured test suites, and post hoc judging. Two basic principles of linguistic structure are relevant for building text mining applications. One is that linguistic structure consists of multiple levels. The other is that every level of linguistic structure is characterized by ambiguity. There are two basic approaches to text mining: rule-based, also known as knowledge-based; and machine-learning-based, also known as statistical. Many systems are hybrids of the two approaches. Shared tasks have had a strong effect on the direction of the field. Like all translational bioinformatics software, text mining software for translational bioinformatics can be considered health-critical and should be subject to the strictest standards of quality assurance and software testing.

Trend Motif: A Graph Mining Approach for Analysis of Dynamic Complex Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, R; McCallen, S; Almaas, E

2007-05-28

Complex networks have been used successfully in scientific disciplines ranging from sociology to microbiology to describe systems of interacting units. Until recently, studies of complex networks have mainly focused on their network topology. However, in many real world applications, the edges and vertices have associated attributes that are frequently represented as vertex or edge weights. Furthermore, these weights are often not static, instead changing with time and forming a time series. Hence, to fully understand the dynamics of the complex network, we have to consider both network topology and related time series data. In this work, we propose a motifmore » mining approach to identify trend motifs for such purposes. Simply stated, a trend motif describes a recurring subgraph where each of its vertices or edges displays similar dynamics over a userdefined period. Given this, each trend motif occurrence can help reveal significant events in a complex system; frequent trend motifs may aid in uncovering dynamic rules of change for the system, and the distribution of trend motifs may characterize the global dynamics of the system. Here, we have developed efficient mining algorithms to extract trend motifs. Our experimental validation using three disparate empirical datasets, ranging from the stock market, world trade, to a protein interaction network, has demonstrated the efficiency and effectiveness of our approach.« less

Location Prediction Based on Transition Probability Matrices Constructing from Sequential Rules for Spatial-Temporal K-Anonymity Dataset

PubMed Central

Liu, Zhao; Zhu, Yunhong; Wu, Chenxue

2016-01-01

Spatial-temporal k-anonymity has become a mainstream approach among techniques for protection of users’ privacy in location-based services (LBS) applications, and has been applied to several variants such as LBS snapshot queries and continuous queries. Analyzing large-scale spatial-temporal anonymity sets may benefit several LBS applications. In this paper, we propose two location prediction methods based on transition probability matrices constructing from sequential rules for spatial-temporal k-anonymity dataset. First, we define single-step sequential rules mined from sequential spatial-temporal k-anonymity datasets generated from continuous LBS queries for multiple users. We then construct transition probability matrices from mined single-step sequential rules, and normalize the transition probabilities in the transition matrices. Next, we regard a mobility model for an LBS requester as a stationary stochastic process and compute the n-step transition probability matrices by raising the normalized transition probability matrices to the power n. Furthermore, we propose two location prediction methods: rough prediction and accurate prediction. The former achieves the probabilities of arriving at target locations along simple paths those include only current locations, target locations and transition steps. By iteratively combining the probabilities for simple paths with n steps and the probabilities for detailed paths with n-1 steps, the latter method calculates transition probabilities for detailed paths with n steps from current locations to target locations. Finally, we conduct extensive experiments, and correctness and flexibility of our proposed algorithm have been verified. PMID:27508502

Stratified sampling design based on data mining.

PubMed

Kim, Yeonkook J; Oh, Yoonhwan; Park, Sunghoon; Cho, Sungzoon; Park, Hayoung

2013-09-01

To explore classification rules based on data mining methodologies which are to be used in defining strata in stratified sampling of healthcare providers with improved sampling efficiency. We performed k-means clustering to group providers with similar characteristics, then, constructed decision trees on cluster labels to generate stratification rules. We assessed the variance explained by the stratification proposed in this study and by conventional stratification to evaluate the performance of the sampling design. We constructed a study database from health insurance claims data and providers' profile data made available to this study by the Health Insurance Review and Assessment Service of South Korea, and population data from Statistics Korea. From our database, we used the data for single specialty clinics or hospitals in two specialties, general surgery and ophthalmology, for the year 2011 in this study. Data mining resulted in five strata in general surgery with two stratification variables, the number of inpatients per specialist and population density of provider location, and five strata in ophthalmology with two stratification variables, the number of inpatients per specialist and number of beds. The percentages of variance in annual changes in the productivity of specialists explained by the stratification in general surgery and ophthalmology were 22% and 8%, respectively, whereas conventional stratification by the type of provider location and number of beds explained 2% and 0.2% of variance, respectively. This study demonstrated that data mining methods can be used in designing efficient stratified sampling with variables readily available to the insurer and government; it offers an alternative to the existing stratification method that is widely used in healthcare provider surveys in South Korea.

Design of foundations with sliding joint at areas affected with underground mining

NASA Astrophysics Data System (ADS)

Matečková, P.; Šmiřáková, M.; Maňásek, P.

2018-04-01

Underground mining always influences also landscape on surface. If there are buildings on the surface they are affected with terrain deformation which comprises terrain inclination, curvature, shift and horizontal deformation. Ostrava – Karvina region is specific with underground mining very close to densely inhabited area. About 25 years ago there were mines even in the city of Ostrava. Recommendations and rules for design of building structures at areas affected with underground mining have been therefore analysed in long term. This paper is focused on deformation action caused by terrain horizontal deformation - expansion or compression. Through the friction between foundation structure and subsoil in footing bottom the foundation structure has to resist significant normal forces. The idea of sliding joint which eliminates the friction and decreases internal forces comes from the last century. Sliding joint made of asphalt belt has been analysed at Faculty of Civil Engineering, VSB – Technical University of Ostrava in long term. The influence of vertical and horizontal load and the effect of temperature in temperature controlled room have been examined. Testing, design and utilization of sliding joint is presented.

A New Approach for Resolving Conflicts in Actionable Behavioral Rules

PubMed Central

Zhu, Dan; Zeng, Daniel

2014-01-01

Knowledge is considered actionable if users can take direct actions based on such knowledge to their advantage. Among the most important and distinctive actionable knowledge are actionable behavioral rules that can directly and explicitly suggest specific actions to take to influence (restrain or encourage) the behavior in the users' best interest. However, in mining such rules, it often occurs that different rules may suggest the same actions with different expected utilities, which we call conflicting rules. To resolve the conflicts, a previous valid method was proposed. However, inconsistency of the measure for rule evaluating may hinder its performance. To overcome this problem, we develop a new method that utilizes rule ranking procedure as the basis for selecting the rule with the highest utility prediction accuracy. More specifically, we propose an integrative measure, which combines the measures of the support and antecedent length, to evaluate the utility prediction accuracies of conflicting rules. We also introduce a tunable weight parameter to allow the flexibility of integration. We conduct several experiments to test our proposed approach and evaluate the sensitivity of the weight parameter. Empirical results indicate that our approach outperforms those from previous research. PMID:25162054

75 FR 6330 - North Dakota Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2010-02-09

... Surface Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period and opportunity for public hearing on proposed amendment. SUMMARY: We are announcing receipt of a proposed... the amendment, and the procedures that we will follow for the public hearing, if one is requested...

77 FR 48429 - Commission Address Change

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-14

....C. 804(3)(C), this rule ``does not substantially affect the rights or obligations of non-agency... Administrative practice and procedure, Civil rights, Equal employment opportunity, Federal buildings and... ON THE BASIS OF HANDICAP IN PROGRAMS OR ACTIVITIES CONDUCTED BY THE FEDERAL MINE SAFETY AND HEALTH...

75 FR 73955 - Penalty Settlement Procedure

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-30

... 1977, or Mine Act. Hearings are held before the Commission's Administrative Law Judges, and appellate... Senate. The Commission is publishing a final rule to streamline the process for settling civil penalties... Commission's civil penalty settlement procedures. 75 FR 21987. The Commission explained that since 2006, the...

43 CFR 4.1361 - Who may file.

Code of Federal Regulations, 2010 CFR

2010-10-01

... Special Rules Applicable to Surface Coal Mining Hearings and Appeals Request for Review of Approval Or... Sale of Rights Granted Under Permit (federal Program; Federal Lands Program; Federal Program for Indian... forth in § 4.1360 may file a request for review of that decision. ...

A Hybrid Data Mining Approach for Credit Card Usage Behavior Analysis

NASA Astrophysics Data System (ADS)

Tsai, Chieh-Yuan

Credit card is one of the most popular e-payment approaches in current online e-commerce. To consolidate valuable customers, card issuers invest a lot of money to maintain good relationship with their customers. Although several efforts have been done in studying card usage motivation, few researches emphasize on credit card usage behavior analysis when time periods change from t to t+1. To address this issue, an integrated data mining approach is proposed in this paper. First, the customer profile and their transaction data at time period t are retrieved from databases. Second, a LabelSOM neural network groups customers into segments and identify critical characteristics for each group. Third, a fuzzy decision tree algorithm is used to construct usage behavior rules of interesting customer groups. Finally, these rules are used to analysis the behavior changes between time periods t and t+1. An implementation case using a practical credit card database provided by a commercial bank in Taiwan is illustrated to show the benefits of the proposed framework.

Agile Text Mining for the 2014 i2b2/UTHealth Cardiac Risk Factors Challenge

PubMed Central

Cormack, James; Nath, Chinmoy; Milward, David; Raja, Kalpana; Jonnalagadda, Siddhartha R

2016-01-01

This paper describes the use of an agile text mining platform (Linguamatics’ Interactive Information Extraction Platform, I2E) to extract document-level cardiac risk factors in patient records as defined in the i2b2/UTHealth 2014 Challenge. The approach uses a data-driven rule-based methodology with the addition of a simple supervised classifier. We demonstrate that agile text mining allows for rapid optimization of extraction strategies, while post-processing can leverage annotation guidelines, corpus statistics and logic inferred from the gold standard data. We also show how data imbalance in a training set affects performance. Evaluation of this approach on the test data gave an F-Score of 91.7%, one percent behind the top performing system. PMID:26209007

Frac Sand Mines Are Preferentially Sited in Unzoned Rural Areas.

PubMed

Locke, Christina

2015-01-01

Shifting markets can cause unexpected, stochastic changes in rural landscapes that may take local communities by surprise. Preferential siting of new industrial facilities in poor areas or in areas with few regulatory restrictions can have implications for environmental sustainability, human health, and social justice. This study focuses on frac sand mining-the mining of high-quality silica sand used in hydraulic fracturing processes for gas and oil extraction. Frac sand mining gained prominence in the 2000s in the upper midwestern United States where nonmetallic mining is regulated primarily by local zoning. I asked whether frac sand mines were more commonly sited in rural townships without formal zoning regulations or planning processes than in those that undertook zoning and planning before the frac sand boom. I also asked if mine prevalence was correlated with socioeconomic differences across townships. After creating a probability surface to map areas most suitable for frac sand mine occurrence, I developed neutral landscape models from which to compare actual mine distributions in zoned and unzoned areas at three different spatial extents. Mines were significantly clustered in unzoned jurisdictions at the statewide level and in 7 of the 8 counties with at least three frac sand mines and some unzoned land. Subsequent regression analyses showed mine prevalence to be uncorrelated with land value, tax rate, or per capita income, but correlated with remoteness and zoning. The predicted mine count in unzoned townships was over two times higher than that in zoned townships. However, the county with the most mines by far was under a county zoning ordinance, perhaps indicating industry preferences for locations with clear, homogenous rules over patchwork regulation. Rural communities can use the case of frac sand mining as motivation to discuss and plan for sudden land-use predicaments, rather than wait to grapple with unfamiliar legal processes during a period of intense conflict.

Data Mining Applied to Analysis of Contraceptive Methods Among College Students.

PubMed

Simões, Priscyla Waleska; Cesconetto, Samuel; Dalló, Eduardo Daminelli; de Souza Pires, Maria Marlene; Comunello, Eros; Borges Tomaz, Felipe; Xavier, Eduardo Pícolo; da Rosa Brunel Alves, Pedro Antonio; Ceretta, Luciane Bisognin; Manenti, Sandra Aparecida

2017-01-01

The aim of this study was to use the Data Mining to analyze the profile of the use of contraceptive methods in a university population. We used a database about sexuality performed on a university population in southern Brazil. The results obtained by the generated rules are largely in line with the literature and epidemiology worldwide, showing significant points of vulnerability in the university population. Validation measures of the study, as such, accuracy, sensitivity, specificity, and area under the ROC curve were higher or at least similar as compared to recent studies using the same methodology.

76 FR 18467 - Pennsylvania Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-04-04

... Surface Mining Reclamation and Enforcement (OSM), Interior. ACTION: Proposed rule; reopening of the public comment period. SUMMARY: We are reopening the public comment period related to an amendment to the... procedures that we will follow for the public hearing, if one is requested. DATES: We will accept written...

43 CFR 4.1373 - Hearing.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 43 Public Lands: Interior 1 2014-10-01 2014-10-01 false Hearing. 4.1373 Section 4.1373 Public Lands: Interior Office of the Secretary of the Interior DEPARTMENT HEARINGS AND APPEALS PROCEDURES Special Rules Applicable to Surface Coal Mining Hearings and Appeals Review of Osm Decisions Proposing to...

20 CFR 725.458 - Depositions; interrogatories.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 20 Employees' Benefits 3 2010-04-01 2010-04-01 false Depositions; interrogatories. 725.458 Section... MINE SAFETY AND HEALTH ACT, AS AMENDED Hearings § 725.458 Depositions; interrogatories. The testimony of any witness or party may be taken by deposition or interrogatory according to the rules of...

20 CFR 725.458 - Depositions; interrogatories.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 20 Employees' Benefits 3 2011-04-01 2011-04-01 false Depositions; interrogatories. 725.458 Section... FEDERAL MINE SAFETY AND HEALTH ACT, AS AMENDED Hearings § 725.458 Depositions; interrogatories. The testimony of any witness or party may be taken by deposition or interrogatory according to the rules of...

43 CFR 3481.1 - General obligations of the operator/lessee.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Federal coal pursuant to the performance standards of the rules of this part, applicable requirements of.... 3481.1 Section 3481.1 Public Lands: Interior Regulations Relating to Public Lands (Continued) BUREAU OF LAND MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) COAL EXPLORATION AND MINING...

43 CFR 3481.1 - General obligations of the operator/lessee.

Code of Federal Regulations, 2014 CFR

2014-10-01

... Federal coal pursuant to the performance standards of the rules of this part, applicable requirements of.... 3481.1 Section 3481.1 Public Lands: Interior Regulations Relating to Public Lands (Continued) BUREAU OF LAND MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) COAL EXPLORATION AND MINING...

43 CFR 3481.1 - General obligations of the operator/lessee.

Code of Federal Regulations, 2012 CFR

2012-10-01

... Federal coal pursuant to the performance standards of the rules of this part, applicable requirements of.... 3481.1 Section 3481.1 Public Lands: Interior Regulations Relating to Public Lands (Continued) BUREAU OF LAND MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) COAL EXPLORATION AND MINING...

43 CFR 3481.1 - General obligations of the operator/lessee.

Code of Federal Regulations, 2013 CFR

2013-10-01

... Federal coal pursuant to the performance standards of the rules of this part, applicable requirements of.... 3481.1 Section 3481.1 Public Lands: Interior Regulations Relating to Public Lands (Continued) BUREAU OF LAND MANAGEMENT, DEPARTMENT OF THE INTERIOR MINERALS MANAGEMENT (3000) COAL EXPLORATION AND MINING...

43 CFR 4.1373 - Hearing.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 43 Public Lands: Interior 1 2010-10-01 2010-10-01 false Hearing. 4.1373 Section 4.1373 Public Lands: Interior Office of the Secretary of the Interior DEPARTMENT HEARINGS AND APPEALS PROCEDURES Special Rules Applicable to Surface Coal Mining Hearings and Appeals Review of Osm Decisions Proposing to...

29 CFR 2700.107 - Discovery.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 29 Labor 9 2013-07-01 2013-07-01 false Discovery. 2700.107 Section 2700.107 Labor Regulations Relating to Labor (Continued) FEDERAL MINE SAFETY AND HEALTH REVIEW COMMISSION PROCEDURAL RULES Simplified Proceedings § 2700.107 Discovery. Discovery is not permitted except as ordered by the Administrative Law Judge. ...

29 CFR 2700.107 - Discovery.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 29 Labor 9 2014-07-01 2014-07-01 false Discovery. 2700.107 Section 2700.107 Labor Regulations Relating to Labor (Continued) FEDERAL MINE SAFETY AND HEALTH REVIEW COMMISSION PROCEDURAL RULES Simplified Proceedings § 2700.107 Discovery. Discovery is not permitted except as ordered by the Administrative Law Judge. ...

29 CFR 2700.107 - Discovery.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 29 Labor 9 2011-07-01 2011-07-01 false Discovery. 2700.107 Section 2700.107 Labor Regulations Relating to Labor (Continued) FEDERAL MINE SAFETY AND HEALTH REVIEW COMMISSION PROCEDURAL RULES Simplified Proceedings § 2700.107 Discovery. Discovery is not permitted except as ordered by the Administrative Law Judge. ...

29 CFR 2700.107 - Discovery.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 29 Labor 9 2012-07-01 2012-07-01 false Discovery. 2700.107 Section 2700.107 Labor Regulations Relating to Labor (Continued) FEDERAL MINE SAFETY AND HEALTH REVIEW COMMISSION PROCEDURAL RULES Simplified Proceedings § 2700.107 Discovery. Discovery is not permitted except as ordered by the Administrative Law Judge. ...

43 CFR 3485.1 - Reports.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 43 Public Lands: Interior 2 2011-10-01 2011-10-01 false Reports. 3485.1 Section 3485.1 Public... OF THE INTERIOR MINERALS MANAGEMENT (3000) COAL EXPLORATION AND MINING OPERATIONS RULES Reports, Royalties and Records § 3485.1 Reports. (a) Exploration reports. The operator/lessee shall file with the...

43 CFR 3485.1 - Reports.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 43 Public Lands: Interior 2 2012-10-01 2012-10-01 false Reports. 3485.1 Section 3485.1 Public... OF THE INTERIOR MINERALS MANAGEMENT (3000) COAL EXPLORATION AND MINING OPERATIONS RULES Reports, Royalties and Records § 3485.1 Reports. (a) Exploration reports. The operator/lessee shall file with the...

43 CFR 3485.1 - Reports.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 43 Public Lands: Interior 2 2013-10-01 2013-10-01 false Reports. 3485.1 Section 3485.1 Public... OF THE INTERIOR MINERALS MANAGEMENT (3000) COAL EXPLORATION AND MINING OPERATIONS RULES Reports, Royalties and Records § 3485.1 Reports. (a) Exploration reports. The operator/lessee shall file with the...

Analysis of adverse events of renal impairment related to platinum-based compounds using the Japanese Adverse Drug Event Report database.

PubMed

Naganuma, Misa; Motooka, Yumi; Sasaoka, Sayaka; Hatahira, Haruna; Hasegawa, Shiori; Fukuda, Akiho; Nakao, Satoshi; Shimada, Kazuyo; Hirade, Koseki; Mori, Takayuki; Yoshimura, Tomoaki; Kato, Takeshi; Nakamura, Mitsuhiro

2018-01-01

Platinum compounds cause several adverse events, such as nephrotoxicity, gastrointestinal toxicity, myelosuppression, ototoxicity, and neurotoxicity. We evaluated the incidence of renal impairment as adverse events are related to the administration of platinum compounds using the Japanese Adverse Drug Event Report database. We analyzed adverse events associated with the use of platinum compounds reported from April 2004 to November 2016. The reporting odds ratio at 95% confidence interval was used to detect the signal for each renal impairment incidence. We evaluated the time-to-onset profile of renal impairment and assessed the hazard type using Weibull shape parameter and used the applied association rule mining technique to discover undetected relationships such as possible risk factor. In total, 430,587 reports in the Japanese Adverse Drug Event Report database were analyzed. The reporting odds ratios (95% confidence interval) for renal impairment resulting from the use of cisplatin, oxaliplatin, carboplatin, and nedaplatin were 2.7 (2.5-3.0), 0.6 (0.5-0.7), 0.8 (0.7-1.0), and 1.3 (0.8-2.1), respectively. The lower limit of the reporting odds ratio (95% confidence interval) for cisplatin was >1. The median (lower-upper quartile) onset time of renal impairment following the use of platinum-based compounds was 6.0-8.0 days. The Weibull shape parameter β and 95% confidence interval upper limit of oxaliplatin were <1. In the association rule mining, the score of lift for patients who were treated with cisplatin and co-administered furosemide, loxoprofen, or pemetrexed was high. Similarly, the scores for patients with hypertension or diabetes mellitus were high. Our findings suggest a potential risk of renal impairment during cisplatin use in real-world setting. The present findings demonstrate that the incidence of renal impairment following cisplatin use should be closely monitored when patients are hypertensive or diabetic, or when they are co-administered furosemide, loxoprofen, or pemetrexed. In addition, healthcare professionals should closely assess a patient's background prior to treatment.

Thromboembolic adverse event study of combined estrogen-progestin preparations using Japanese Adverse Drug Event Report database

PubMed Central

Hasegawa, Shiori; Matsui, Toshinobu; Hane, Yuuki; Abe, Junko; Hatahira, Haruna; Motooka, Yumi; Sasaoka, Sayaka; Fukuda, Akiho; Naganuma, Misa; Hirade, Kouseki; Takahashi, Yukiko; Kinosada, Yasutomi

2017-01-01

Combined estrogen-progestin preparations (CEPs) are associated with thromboembolic (TE) side effects. The aim of this study was to evaluate the incidence of TE using the Japanese Adverse Drug Event Report (JADER) database. Adverse events recorded from April 2004 to November 2014 in the JADER database were obtained from the Pharmaceuticals and Medical Devices Agency (PMDA) website (www.pmda.go.jp). We calculated the reporting odds ratios (RORs) of suspected CEPs, analyzed the time-to-onset profile, and assessed the hazard type using Weibull shape parameter (WSP). Furthermore, we used the applied association rule mining technique to discover undetected relationships such as the possible risk factors. The total number of reported cases in the JADER contained was 338,224. The RORs (95% confidential interval, CI) of drospirenone combined with ethinyl estradiol (EE, Dro-EE), norethisterone with EE (Ne-EE), levonorgestrel with EE (Lev-EE), desogestrel with EE (Des-EE), and norgestrel with EE (Nor-EE) were 56.2 (44.3–71.4), 29.1 (23.5–35.9), 42.9 (32.3–57.0), 44.7 (32.7–61.1), and 38.6 (26.3–56.7), respectively. The medians (25%–75%) of the time-to-onset of Dro-EE, Ne-EE, Lev-EE, Des-EE, and Nor-EE were 150.0 (75.3–314.0), 128.0 (27.0–279.0), 204.0 (44.0–660.0), 142.0 (41.3–344.0), and 16.5 (8.8–32.0) days, respectively. The 95% CIs of the WSP-β for Ne-EE, Lev-EE, and Nor-EE were lower and excluded 1. Association rule mining indicated that patients with anemia had a potential risk of developing a TE when using CEPs. Our results suggest that it is important to monitor patients administered CEP for TE. Careful observation is recommended, especially for those using Nor-EE, and this information may be useful for efficient therapeutic planning. PMID:28732067

Frac Sand Mines Are Preferentially Sited in Unzoned Rural Areas

PubMed Central

Locke, Christina

2015-01-01

Shifting markets can cause unexpected, stochastic changes in rural landscapes that may take local communities by surprise. Preferential siting of new industrial facilities in poor areas or in areas with few regulatory restrictions can have implications for environmental sustainability, human health, and social justice. This study focuses on frac sand mining—the mining of high-quality silica sand used in hydraulic fracturing processes for gas and oil extraction. Frac sand mining gained prominence in the 2000s in the upper midwestern United States where nonmetallic mining is regulated primarily by local zoning. I asked whether frac sand mines were more commonly sited in rural townships without formal zoning regulations or planning processes than in those that undertook zoning and planning before the frac sand boom. I also asked if mine prevalence was correlated with socioeconomic differences across townships. After creating a probability surface to map areas most suitable for frac sand mine occurrence, I developed neutral landscape models from which to compare actual mine distributions in zoned and unzoned areas at three different spatial extents. Mines were significantly clustered in unzoned jurisdictions at the statewide level and in 7 of the 8 counties with at least three frac sand mines and some unzoned land. Subsequent regression analyses showed mine prevalence to be uncorrelated with land value, tax rate, or per capita income, but correlated with remoteness and zoning. The predicted mine count in unzoned townships was over two times higher than that in zoned townships. However, the county with the most mines by far was under a county zoning ordinance, perhaps indicating industry preferences for locations with clear, homogenous rules over patchwork regulation. Rural communities can use the case of frac sand mining as motivation to discuss and plan for sudden land-use predicaments, rather than wait to grapple with unfamiliar legal processes during a period of intense conflict. PMID:26136238

Study on Strata Behavior Regularity of 1301 Face in Thick Bedrock of Wei - qiang Coal Mine

NASA Astrophysics Data System (ADS)

Gu, Shuancheng; Yao, Boyu

2017-09-01

In order to ensure the safe and efficient production of the thick bedrock face, the rule of the strata behavior of the thick bedrock face is discussed through the observation of the strata pressure of the 1301 first mining face in Wei qiang coal mine. The initial face is to press the average distance of 50.75m, the periodic weighting is to press the average distance of 12.1m; during the normal mining period, although the upper roof can not be broken at the same time, but the pressure step is basically the same; the working face for the first weighting and periodical weighting is more obvious to the change of pressure step change, when the pressure of the working face is coming, the stent force increased significantly, but there are still part of the stent work resistance exceeds the rated working resistance, low stability, still need to strengthen management.

Dynamic Task Optimization in Remote Diabetes Monitoring Systems.

PubMed

Suh, Myung-Kyung; Woodbridge, Jonathan; Moin, Tannaz; Lan, Mars; Alshurafa, Nabil; Samy, Lauren; Mortazavi, Bobak; Ghasemzadeh, Hassan; Bui, Alex; Ahmadi, Sheila; Sarrafzadeh, Majid

2012-09-01

Diabetes is the seventh leading cause of death in the United States, but careful symptom monitoring can prevent adverse events. A real-time patient monitoring and feedback system is one of the solutions to help patients with diabetes and their healthcare professionals monitor health-related measurements and provide dynamic feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the domain of remote health monitoring. This paper presents a wireless health project (WANDA) that leverages sensor technology and wireless communication to monitor the health status of patients with diabetes. The WANDA dynamic task management function applies data analytics in real-time to discretize continuous features, applying data clustering and association rule mining techniques to manage a sliding window size dynamically and to prioritize required user tasks. The developed algorithm minimizes the number of daily action items required by patients with diabetes using association rules that satisfy a minimum support, confidence and conditional probability thresholds. Each of these tasks maximizes information gain, thereby improving the overall level of patient adherence and satisfaction. Experimental results from applying EM-based clustering and Apriori algorithms show that the developed algorithm can predict further events with higher confidence levels and reduce the number of user tasks by up to 76.19 %.

Dynamic Task Optimization in Remote Diabetes Monitoring Systems

PubMed Central

Suh, Myung-kyung; Woodbridge, Jonathan; Moin, Tannaz; Lan, Mars; Alshurafa, Nabil; Samy, Lauren; Mortazavi, Bobak; Ghasemzadeh, Hassan; Bui, Alex; Ahmadi, Sheila; Sarrafzadeh, Majid

2016-01-01

Diabetes is the seventh leading cause of death in the United States, but careful symptom monitoring can prevent adverse events. A real-time patient monitoring and feedback system is one of the solutions to help patients with diabetes and their healthcare professionals monitor health-related measurements and provide dynamic feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the domain of remote health monitoring. This paper presents a wireless health project (WANDA) that leverages sensor technology and wireless communication to monitor the health status of patients with diabetes. The WANDA dynamic task management function applies data analytics in real-time to discretize continuous features, applying data clustering and association rule mining techniques to manage a sliding window size dynamically and to prioritize required user tasks. The developed algorithm minimizes the number of daily action items required by patients with diabetes using association rules that satisfy a minimum support, confidence and conditional probability thresholds. Each of these tasks maximizes information gain, thereby improving the overall level of patient adherence and satisfaction. Experimental results from applying EM-based clustering and Apriori algorithms show that the developed algorithm can predict further events with higher confidence levels and reduce the number of user tasks by up to 76.19 %. PMID:27617297

Automatic detection of referral patients due to retinal pathologies through data mining.

PubMed

Quellec, Gwenolé; Lamard, Mathieu; Erginay, Ali; Chabouis, Agnès; Massin, Pascale; Cochener, Béatrice; Cazuguel, Guy

2016-04-01

With the increased prevalence of retinal pathologies, automating the detection of these pathologies is becoming more and more relevant. In the past few years, many algorithms have been developed for the automated detection of a specific pathology, typically diabetic retinopathy, using eye fundus photography. No matter how good these algorithms are, we believe many clinicians would not use automatic detection tools focusing on a single pathology and ignoring any other pathology present in the patient's retinas. To solve this issue, an algorithm for characterizing the appearance of abnormal retinas, as well as the appearance of the normal ones, is presented. This algorithm does not focus on individual images: it considers examination records consisting of multiple photographs of each retina, together with contextual information about the patient. Specifically, it relies on data mining in order to learn diagnosis rules from characterizations of fundus examination records. The main novelty is that the content of examination records (images and context) is characterized at multiple levels of spatial and lexical granularity: 1) spatial flexibility is ensured by an adaptive decomposition of composite retinal images into a cascade of regions, 2) lexical granularity is ensured by an adaptive decomposition of the feature space into a cascade of visual words. This multigranular representation allows for great flexibility in automatically characterizing normality and abnormality: it is possible to generate diagnosis rules whose precision and generalization ability can be traded off depending on data availability. A variation on usual data mining algorithms, originally designed to mine static data, is proposed so that contextual and visual data at adaptive granularity levels can be mined. This framework was evaluated in e-ophtha, a dataset of 25,702 examination records from the OPHDIAT screening network, as well as in the publicly-available Messidor dataset. It was successfully applied to the detection of patients that should be referred to an ophthalmologist and also to the specific detection of several pathologies. Copyright © 2016 Elsevier B.V. All rights reserved.

Implementation of Data Mining to Analyze Drug Cases Using C4.5 Decision Tree

NASA Astrophysics Data System (ADS)

Wahyuni, Sri

2018-03-01

Data mining was the process of finding useful information from a large set of databases. One of the existing techniques in data mining was classification. The method used was decision tree method and algorithm used was C4.5 algorithm. The decision tree method was a method that transformed a very large fact into a decision tree which was presenting the rules. Decision tree method was useful for exploring data, as well as finding a hidden relationship between a number of potential input variables with a target variable. The decision tree of the C4.5 algorithm was constructed with several stages including the selection of attributes as roots, created a branch for each value and divided the case into the branch. These stages would be repeated for each branch until all the cases on the branch had the same class. From the solution of the decision tree there would be some rules of a case. In this case the researcher classified the data of prisoners at Labuhan Deli prison to know the factors of detainees committing criminal acts of drugs. By applying this C4.5 algorithm, then the knowledge was obtained as information to minimize the criminal acts of drugs. From the findings of the research, it was found that the most influential factor of the detainee committed the criminal act of drugs was from the address variable.

43 CFR 4.1103 - Eligibility to practice.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 43 Public Lands: Interior 1 2010-10-01 2010-10-01 false Eligibility to practice. 4.1103 Section 4.1103 Public Lands: Interior Office of the Secretary of the Interior DEPARTMENT HEARINGS AND APPEALS PROCEDURES Special Rules Applicable to Surface Coal Mining Hearings and Appeals General Provisions § 4.1103...

43 CFR 4.1309 - Petition for discretionary review.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 43 Public Lands: Interior 1 2010-10-01 2010-10-01 false Petition for discretionary review. 4.1309... APPEALS PROCEDURES Special Rules Applicable to Surface Coal Mining Hearings and Appeals Petitions for Review of Proposed Individual Civil Penalty Assessments Under Section 518(f) of the Act § 4.1309 Petition...

43 CFR 4.1130 - Discovery methods.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 43 Public Lands: Interior 1 2013-10-01 2013-10-01 false Discovery methods. 4.1130 Section 4.1130... Special Rules Applicable to Surface Coal Mining Hearings and Appeals Discovery § 4.1130 Discovery methods. Parties may obtain discovery by one or more of the following methods— (a) Depositions upon oral...

43 CFR 4.1130 - Discovery methods.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 43 Public Lands: Interior 1 2010-10-01 2010-10-01 false Discovery methods. 4.1130 Section 4.1130... Special Rules Applicable to Surface Coal Mining Hearings and Appeals Discovery § 4.1130 Discovery methods. Parties may obtain discovery by one or more of the following methods— (a) Depositions upon oral...

43 CFR 4.1130 - Discovery methods.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 43 Public Lands: Interior 1 2011-10-01 2011-10-01 false Discovery methods. 4.1130 Section 4.1130... Special Rules Applicable to Surface Coal Mining Hearings and Appeals Discovery § 4.1130 Discovery methods. Parties may obtain discovery by one or more of the following methods— (a) Depositions upon oral...

43 CFR 4.1130 - Discovery methods.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 43 Public Lands: Interior 1 2014-10-01 2014-10-01 false Discovery methods. 4.1130 Section 4.1130... Special Rules Applicable to Surface Coal Mining Hearings and Appeals Discovery § 4.1130 Discovery methods. Parties may obtain discovery by one or more of the following methods— (a) Depositions upon oral...

43 CFR 4.1130 - Discovery methods.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 43 Public Lands: Interior 1 2012-10-01 2011-10-01 true Discovery methods. 4.1130 Section 4.1130... Special Rules Applicable to Surface Coal Mining Hearings and Appeals Discovery § 4.1130 Discovery methods. Parties may obtain discovery by one or more of the following methods— (a) Depositions upon oral...

20 CFR 410.707 - Hearings and appeals.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 20 Employees' Benefits 2 2011-04-01 2011-04-01 false Hearings and appeals. 410.707 Section 410.707 Employees' Benefits SOCIAL SECURITY ADMINISTRATION FEDERAL COAL MINE HEALTH AND SAFETY ACT OF 1969, TITLE IV-BLACK LUNG BENEFITS (1969- ) Rules for the Review of Denied and Pending Claims Under the Black Lung...

20 CFR 410.707 - Hearings and appeals.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 20 Employees' Benefits 2 2010-04-01 2010-04-01 false Hearings and appeals. 410.707 Section 410.707 Employees' Benefits SOCIAL SECURITY ADMINISTRATION FEDERAL COAL MINE HEALTH AND SAFETY ACT OF 1969, TITLE IV-BLACK LUNG BENEFITS (1969- ) Rules for the Review of Denied and Pending Claims Under the Black Lung...

20 CFR 410.705 - Duplicate claims.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 20 Employees' Benefits 2 2011-04-01 2011-04-01 false Duplicate claims. 410.705 Section 410.705 Employees' Benefits SOCIAL SECURITY ADMINISTRATION FEDERAL COAL MINE HEALTH AND SAFETY ACT OF 1969, TITLE IV-BLACK LUNG BENEFITS (1969- ) Rules for the Review of Denied and Pending Claims Under the Black Lung...

43 CFR 4.1355 - Burden of proof.

Code of Federal Regulations, 2010 CFR

2010-10-01

... Special Rules Applicable to Surface Coal Mining Hearings and Appeals Request for Hearing on A Preliminary.... 1260(c) (federal Program; Federal Lands Program; Federal Program for Indian Lands) § 4.1355 Burden of... comply with the Act, its implementing regulations, the regulatory program, or the permit. [67 FR 61511...

30 CFR 948.15 - Approval of West Virginia regulatory program amendments.

Code of Federal Regulations, 2011 CFR

2011-07-01

... amendments. 948.15 Section 948.15 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT... approving all or portions of those amendments in the Federal Register, and the State statutory or regulatory... to those final rules identify and discuss any assumptions underlying approval, any conditions placed...

Robert C. Byrd Mine Safety Protection Act of 2010

THOMAS, 111th Congress

Rep. Miller, George [D-CA-7

2010-12-03

House - 12/08/2010 On motion to suspend the rules and pass the bill, as amended Failed by the Yeas and Nays: (2/3 required): 214 - 193 (Roll no. 616). (All Actions) Tracker: This bill has the status Failed HouseHere are the steps for Status of Legislation:

20 CFR 410.700 - Background.

Code of Federal Regulations, 2011 CFR

2011-04-01

...' Benefits SOCIAL SECURITY ADMINISTRATION FEDERAL COAL MINE HEALTH AND SAFETY ACT OF 1969, TITLE IV-BLACK... claim reviewed under this Act. The purpose of the subpart G is to explain the changes and the procedures, and rules which are applicable with regard to the Social Security Administration's review of part B...

26 CFR 1.616-1 - Development expenditures.

Code of Federal Regulations, 2013 CFR

2013-04-01

... (CONTINUED) INCOME TAXES (CONTINUED) Natural Resources § 1.616-1 Development expenditures. (a) General rule... taxpayer for the development of a mine or other natural deposit (other than an oil or gas well... natural deposit. Under section 616(b), the taxpayer may elect to defer development expenditures made in...

26 CFR 1.616-1 - Development expenditures.

Code of Federal Regulations, 2014 CFR

2014-04-01

...) INCOME TAXES (CONTINUED) Natural Resources § 1.616-1 Development expenditures. (a) General rule. Section... taxpayer for the development of a mine or other natural deposit (other than an oil or gas well... natural deposit. Under section 616(b), the taxpayer may elect to defer development expenditures made in...

Discrimination-Aware Classifiers for Student Performance Prediction

ERIC Educational Resources Information Center

Luo, Ling; Koprinska, Irena; Liu, Wei

2015-01-01

In this paper we consider discrimination-aware classification of educational data. Mining and using rules that distinguish groups of students based on sensitive attributes such as gender and nationality may lead to discrimination. It is desirable to keep the sensitive attributes during the training of a classifier to avoid information loss but…

43 CFR 4.1362 - Where to file; when to file.

Code of Federal Regulations, 2010 CFR

2010-10-01

... APPEALS PROCEDURES Special Rules Applicable to Surface Coal Mining Hearings and Appeals Request for Review... Transfer, Assignment Or Sale of Rights Granted Under Permit (federal Program; Federal Lands Program... file; when to file. (a) The request for review shall be filed with the Hearings Division, Office of...