Interesting association rule mining with consistent and inconsistent rule detection from big sales data in distributed environment

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Dinesh J. Prajapati

2017-06-01

Full Text Available Nowadays, there is an increasing demand in mining interesting patterns from the big data. The process of analyzing such a huge amount of data is really computationally complex task when using traditional methods. The overall purpose of this paper is in twofold. First, this paper presents a novel approach to identify consistent and inconsistent association rules from sales data located in distributed environment. Secondly, the paper also overcomes the main memory bottleneck and computing time overhead of single computing system by applying computations to multi node cluster. The proposed method initially extracts frequent itemsets for each zone using existing distributed frequent pattern mining algorithms. The paper also compares the time efficiency of Mapreduce based frequent pattern mining algorithm with Count Distribution Algorithm (CDA and Fast Distributed Mining (FDM algorithms. The association generated from frequent itemsets are too large that it becomes complex to analyze it. Thus, Mapreduce based consistent and inconsistent rule detection (MR-CIRD algorithm is proposed to detect the consistent and inconsistent rules from big data and provide useful and actionable knowledge to the domain experts. These pruned interesting rules also give useful knowledge for better marketing strategy as well. The extracted consistent and inconsistent rules are evaluated and compared based on different interestingness measures presented together with experimental results that lead to the final conclusions.

Analysis of mesenchymal stem cell differentiation in vitro using classification association rule mining.

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Wang, Weiqi; Wang, Yanbo Justin; Bañares-Alcántara, René; Coenen, Frans; Cui, Zhanfeng

2009-12-01

In this paper, data mining is used to analyze the data on the differentiation of mammalian Mesenchymal Stem Cells (MSCs), aiming at discovering known and hidden rules governing MSC differentiation, following the establishment of a web-based public database containing experimental data on the MSC proliferation and differentiation. To this effect, a web-based public interactive database comprising the key parameters which influence the fate and destiny of mammalian MSCs has been constructed and analyzed using Classification Association Rule Mining (CARM) as a data-mining technique. The results show that the proposed approach is technically feasible and performs well with respect to the accuracy of (classification) prediction. Key rules mined from the constructed MSC database are consistent with experimental observations, indicating the validity of the method developed and the first step in the application of data mining to the study of MSCs.

An Efficient Association Rule Hiding Algorithm for Privacy Preserving Data Mining

OpenAIRE

Yogendra Kumar Jain,; Vinod Kumar Yadav,; Geetika S. Panday

2011-01-01

The security of the large database that contains certain crucial information, it will become a serious issue when sharing data to the network against unauthorized access. Privacy preserving data mining is a new research trend in privacy data for data mining and statistical database. Association analysis is a powerful toolfor discovering relationships which are hidden in large database. Association rules hiding algorithms get strong and efficient performance for protecting confidential and cru...

Quantum algorithm for association rules mining

Science.gov (United States)

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 ) .

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

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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.

Effect of Temporal Relationships in Associative Rule Mining for Web Log Data

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Mohd Khairudin, Nazli; Mustapha, Aida

2014-01-01

The advent of web-based applications and services has created such diverse and voluminous web log data stored in web servers, proxy servers, client machines, or organizational databases. This paper attempts to investigate the effect of temporal attribute in relational rule mining for web log data. We incorporated the characteristics of time in the rule mining process and analysed the effect of various temporal parameters. The rules generated from temporal relational rule mining are then compared against the rules generated from the classical rule mining approach such as the Apriori and FP-Growth algorithms. The results showed that by incorporating the temporal attribute via time, the number of rules generated is subsequently smaller but is comparable in terms of quality. PMID:24587757

Evolving temporal association rules with genetic algorithms

OpenAIRE

Matthews, Stephen G.; Gongora, Mario A.; Hopgood, Adrian A.

2010-01-01

A novel framework for mining temporal association rules by discovering itemsets with a genetic algorithm is introduced. Metaheuristics have been applied to association rule mining, we show the efficacy of extending this to another variant - temporal association rule mining. Our framework is an enhancement to existing temporal association rule mining methods as it employs a genetic algorithm to simultaneously search the rule space and temporal space. A methodology for validating the ability of...

Study on the Method of Association Rules Mining Based on Genetic Algorithm and Application in Analysis of Seawater Samples

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Qiuhong Sun

2014-04-01

Full Text Available Based on the data mining research, the data mining based on genetic algorithm method, the genetic algorithm is briefly introduced, while the genetic algorithm based on two important theories and theoretical templates principle implicit parallelism is also discussed. Focuses on the application of genetic algorithms for association rule mining method based on association rule mining, this paper proposes a genetic algorithm fitness function structure, data encoding, such as the title of the improvement program, in particular through the early issues study, proposed the improved adaptive Pc, Pm algorithm is applied to the genetic algorithm, thereby improving efficiency of the algorithm. Finally, a genetic algorithm based association rule mining algorithm, and be applied in sea water samples database in data mining and prove its effective.

Fuzzy OLAP association rules mining-based modular reinforcement learning approach for multiagent systems.

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Kaya, Mehmet; Alhajj, Reda

2005-04-01

Multiagent systems and data mining have recently attracted considerable attention in the field of computing. Reinforcement learning is the most commonly used learning process for multiagent systems. However, it still has some drawbacks, including modeling other learning agents present in the domain as part of the state of the environment, and some states are experienced much less than others, or some state-action pairs are never visited during the learning phase. Further, before completing the learning process, an agent cannot exhibit a certain behavior in some states that may be experienced sufficiently. In this study, we propose a novel multiagent learning approach to handle these problems. Our approach is based on utilizing the mining process for modular cooperative learning systems. It incorporates fuzziness and online analytical processing (OLAP) based mining to effectively process the information reported by agents. First, we describe a fuzzy data cube OLAP architecture which facilitates effective storage and processing of the state information reported by agents. This way, the action of the other agent, not even in the visual environment. of the agent under consideration, can simply be predicted by extracting online association rules, a well-known data mining technique, from the constructed data cube. Second, we present a new action selection model, which is also based on association rules mining. Finally, we generalize not sufficiently experienced states, by mining multilevel association rules from the proposed fuzzy data cube. Experimental results obtained on two different versions of a well-known pursuit domain show the robustness and effectiveness of the proposed fuzzy OLAP mining based modular learning approach. Finally, we tested the scalability of the approach presented in this paper and compared it with our previous work on modular-fuzzy Q-learning and ordinary Q-learning.

From Intra-transaction to Generalized Inter-transaction: Landscaping Multidimensional Contexts in Association Rule Mining

NARCIS (Netherlands)

Li, Q; Feng, L.; Wong, A.K.Y.

The problem of mining multidimensional inter-transactional association rules was recently introduced in [ACM Trans. Inform. Syst. 18(4) (2000) 423; Proc. of the ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, Seattle, Washington, June 1998, p. 12:1]. It extends the

Using fuzzy association rule mining in cancer classification

International Nuclear Information System (INIS)

Mahmoodian, Hamid; Marhaban, M.H.; Abdulrahim, Raha; Rosli, Rozita; Saripan, Iqbal

2011-01-01

Full text: The classification of the cancer tumors based on gene expression profiles has been extensively studied in numbers of studies. A wide variety of cancer datasets have been implemented by the various methods of gene selec tion and classification to identify the behavior of the genes in tumors and find the relationships between them and outcome of diseases. Interpretability of the model, which is developed by fuzzy rules and linguistic variables in this study, has been rarely considered. In addition, creating a fuzzy classifier with high performance in classification that uses a subset of significant genes which have been selected by different types of gene selection methods is another goal of this study. A new algorithm has been developed to identify the fuzzy rules and significant genes based on fuzzy association rule mining. At first, different subset of genes which have been selected by different methods, were used to generate primary fuzzy classifiers separately and then proposed algorithm was implemented to mix the genes which have been associated in the primary classifiers and generate a new classifier. The results show that fuzzy classifier can classify the tumors with high performance while presenting the relationships between the genes by linguistic variables

Improving Intrusion Detection System Based on Snort Rules for Network Probe Attacks Detection with Association Rules Technique of Data Mining

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Nattawat Khamphakdee

2015-07-01

Full Text Available The intrusion detection system (IDS is an important network security tool for securing computer and network systems. It is able to detect and monitor network traffic data. Snort IDS is an open-source network security tool. It can search and match rules with network traffic data in order to detect attacks, and generate an alert. However, the Snort IDS  can detect only known attacks. Therefore, we have proposed a procedure for improving Snort IDS rules, based on the association rules data mining technique for detection of network probe attacks.  We employed the MIT-DARPA 1999 data set for the experimental evaluation. Since behavior pattern traffic data are both normal and abnormal, the abnormal behavior data is detected by way of the Snort IDS. The experimental results showed that the proposed Snort IDS rules, based on data mining detection of network probe attacks, proved more efficient than the original Snort IDS rules, as well as icmp.rules and icmp-info.rules of Snort IDS.  The suitable parameters for the proposed Snort IDS rules are defined as follows: Min_sup set to 10%, and Min_conf set to 100%, and through the application of eight variable attributes. As more suitable parameters are applied, higher accuracy is achieved.

Decision mining revisited - Discovering overlapping rules

NARCIS (Netherlands)

Mannhardt, Felix; De Leoni, Massimiliano; Reijers, Hajo A.; Van Der Aalst, Wil M P

2016-01-01

Decision mining enriches process models with rules underlying decisions in processes using historical process execution data. Choices between multiple activities are specified through rules defined over process data. Existing decision mining methods focus on discovering mutually-exclusive rules,

Decision Mining Revisited - Discovering Overlapping Rules

NARCIS (Netherlands)

Mannhardt, F.; De Leoni, M.; Reijers, H.A.; van der Aalst, W.M.P.; Nurcan, S.; Soffer, P.; Bajec, M.; Eder, J.

2016-01-01

Decision mining enriches process models with rules underlying decisions in processes using historical process execution data. Choices between multiple activities are specified through rules defined over process data. Existing decision mining methods focus on discovering mutually-exclusive rules,

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

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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.

A Novel Method of Interestingness Measures for Association Rules Mining Based on Profit

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Chunhua Ju

2015-01-01

Full Text Available Association rules mining is an important topic in the domain of data mining and knowledge discovering. Some papers have presented several interestingness measure methods; the most typical are Support, Confidence, Lift, Improve, and so forth. But their limitations are obvious, like no objective criterion, lack of statistical base, disability of defining negative relationship, and so forth. This paper proposes three new methods, Bi-lift, Bi-improve, and Bi-confidence, for Lift, Improve, and Confidence, respectively. Then, on the basis of utility function and the executing cost of rules, we propose interestingness function based on profit (IFBP considering subjective preferences and characteristics of specific application object. Finally, a novel measure framework is proposed to improve the traditional one through experimental analysis. In conclusion, the new methods and measure framework are prior to the traditional ones in the aspects of objective criterion, comprehensive definition, and practical application.

Interestingness measures and strategies for mining multi-ontology multi-level association rules from gene ontology annotations for the discovery of new GO relationships.

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Manda, Prashanti; McCarthy, Fiona; Bridges, Susan M

2013-10-01

The Gene Ontology (GO), a set of three sub-ontologies, is one of the most popular bio-ontologies used for describing gene product characteristics. GO annotation data containing terms from multiple sub-ontologies and at different levels in the ontologies is an important source of implicit relationships between terms from the three sub-ontologies. Data mining techniques such as association rule mining that are tailored to mine from multiple ontologies at multiple levels of abstraction are required for effective knowledge discovery from GO annotation data. We present a data mining approach, Multi-ontology data mining at All Levels (MOAL) that uses the structure and relationships of the GO to mine multi-ontology multi-level association rules. We introduce two interestingness measures: Multi-ontology Support (MOSupport) and Multi-ontology Confidence (MOConfidence) customized to evaluate multi-ontology multi-level association rules. We also describe a variety of post-processing strategies for pruning uninteresting rules. We use publicly available GO annotation data to demonstrate our methods with respect to two applications (1) the discovery of co-annotation suggestions and (2) the discovery of new cross-ontology relationships. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

Decision Mining Revisited – Discovering Overlapping Rules

NARCIS (Netherlands)

Mannhardt, F.; de Leoni, M.; Reijers, H.A.; van der Aalst, W.M.P.

2016-01-01

Decision mining enriches process models with rules underlying decisions in processes using historical process execution data. Choices between multiple activities are specified through rules defined over process data. Existing decision mining methods focus on discovering mutually-exclusive rules,

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

International Nuclear Information System (INIS)

Chaves, R; Gorriz, J M; Ramirez, J; Illan, I A; Salas-Gonzalez, D; Gomez-RIo, M

2011-01-01

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.

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

Science.gov (United States)

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.

New probabilistic interest measures for association rules

OpenAIRE

Hahsler, Michael; Hornik, Kurt

2008-01-01

Mining association rules is an important technique for discovering meaningful patterns in transaction databases. Many different measures of interestingness have been proposed for association rules. However, these measures fail to take the probabilistic properties of the mined data into account. In this paper, we start with presenting a simple probabilistic framework for transaction data which can be used to simulate transaction data when no associations are present. We use such data and a rea...

Class Association Rule Pada Metode Associative Classification

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Eka Karyawati

2011-11-01

Full Text Available Frequent patterns (itemsets discovery is an important problem in associative classification rule mining.  Differents approaches have been proposed such as the Apriori-like, Frequent Pattern (FP-growth, and Transaction Data Location (Tid-list Intersection algorithm. This paper focuses on surveying and comparing the state of the art associative classification techniques with regards to the rule generation phase of associative classification algorithms.  This phase includes frequent itemsets discovery and rules mining/extracting methods to generate the set of class association rules (CARs.  There are some techniques proposed to improve the rule generation method.  A technique by utilizing the concepts of discriminative power of itemsets can reduce the size of frequent itemset.  It can prune the useless frequent itemsets. The closed frequent itemset concept can be utilized to compress the rules to be compact rules.  This technique may reduce the size of generated rules.  Other technique is in determining the support threshold value of the itemset. Specifying not single but multiple support threshold values with regard to the class label frequencies can give more appropriate support threshold value.  This technique may generate more accurate rules. Alternative technique to generate rule is utilizing the vertical layout to represent dataset.  This method is very effective because it only needs one scan over dataset, compare with other techniques that need multiple scan over dataset.   However, one problem with these approaches is that the initial set of tid-lists may be too large to fit into main memory. It requires more sophisticated techniques to compress the tid-lists.

Mining rare associations between biological ontologies.

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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.

Mining rare associations between biological ontologies.

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Fernando Benites

Full Text Available 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.

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

OpenAIRE

Batal, Iyad; Cooper, Gregory; Hauskrecht, Milos

2012-01-01

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 association rule mining-based framework for understanding lifestyle risk behaviors.

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So Hyun Park

Full Text Available OBJECTIVES: This study investigated the prevalence and patterns of lifestyle risk behaviors in Korean adults. METHODS: We utilized data from the Fourth Korea National Health and Nutrition Examination Survey for 14,833 adults (>20 years of age. We used association rule mining to analyze patterns of lifestyle risk behaviors by characterizing non-adherence to public health recommendations related to the Alameda 7 health behaviors. The study variables were current smoking, heavy drinking, physical inactivity, obesity, inadequate sleep, breakfast skipping, and frequent snacking. RESULTS: Approximately 72% of Korean adults exhibited two or more lifestyle risk behaviors. Among women, current smoking, obesity, and breakfast skipping were associated with inadequate sleep. Among men, breakfast skipping with additional risk behaviors such as physical inactivity, obesity, and inadequate sleep was associated with current smoking. Current smoking with additional risk behaviors such as inadequate sleep or breakfast skipping was associated with physical inactivity. CONCLUSION: Lifestyle risk behaviors are intercorrelated in Korea. Information on patterns of lifestyle risk behaviors could assist in planning interventions targeted at multiple behaviors simultaneously.

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

Science.gov (United States)

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.

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

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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.

Association-rule-based tuberculosis disease diagnosis

Science.gov (United States)

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.

A Stock Trading Recommender System Based on Temporal Association Rule Mining

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Binoy B. Nair

2015-04-01

Full Text Available Recommender systems capable of discovering patterns in stock price movements and generating stock recommendations based on the patterns thus discovered can significantly supplement the decision-making process of a stock trader. Such recommender systems are of great significance to a layperson who wishes to profit by stock trading even while not possessing the skill or expertise of a seasoned trader. A genetic algorithm optimized Symbolic Aggregate approXimation (SAX–Apriori based stock trading recommender system, which can mine temporal association rules from the stock price data set to generate stock trading recommendations, is presented in this article. The proposed system is validated on 12 different data sets. The results indicate that the proposed system significantly outperforms the passive buy-and-hold strategy, offering scope for a layperson to successfully invest in capital markets.

Sanitizing sensitive association rules using fuzzy correlation scheme

International Nuclear Information System (INIS)

Hameed, S.; Shahzad, F.; Asghar, S.

2013-01-01

Data mining is used to extract useful information hidden in the data. Sometimes this extraction of information leads to revealing sensitive information. Privacy preservation in Data Mining is a process of sanitizing sensitive information. This research focuses on sanitizing sensitive rules discovered in quantitative data. The proposed scheme, Privacy Preserving in Fuzzy Association Rules (PPFAR) is based on fuzzy correlation analysis. In this work, fuzzy set concept is integrated with fuzzy correlation analysis and Apriori algorithm to mark interesting fuzzy association rules. The identified rules are called sensitive. For sanitization, we use modification technique where we substitute maximum value of fuzzy items with zero, which occurs most frequently. Experiments demonstrate that PPFAR method hides sensitive rules with minimum modifications. The technique also maintains the modified data's quality. The PPFAR scheme has applications in various domains e.g. temperature control, medical analysis, travel time prediction, genetic behavior prediction etc. We have validated the results on medical dataset. (author)

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

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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.

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

Science.gov (United States)

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.

In-Depth Analysis of Energy Efficiency Related Factors in Commercial Buildings Using Data Cube and Association Rule Mining

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Byeongjoon Noh

2017-11-01

Full Text Available Significant amounts of energy are consumed in the commercial building sector, resulting in various adverse environmental issues. To reduce energy consumption and improve energy efficiency in commercial buildings, it is necessary to develop effective methods for analyzing building energy use. In this study, we propose a data cube model combined with association rule mining for more flexible and detailed analysis of building energy consumption profiles using the Commercial Buildings Energy Consumption Survey (CBECS dataset, which has accumulated over 6700 existing commercial buildings across the U.S.A. Based on the data cube model, a multidimensional commercial sector building energy analysis was performed based upon on-line analytical processing (OLAP operations to assess the energy efficiency according to building factors with various levels of abstraction. Furthermore, the proposed analysis system provided useful information that represented a set of energy efficient combinations by applying the association rule mining method. We validated the feasibility and applicability of the proposed analysis model by structuring a building energy analysis system and applying it to different building types, weather conditions, composite materials, and heating/cooling systems of the multitude of commercial buildings classified in the CBECS dataset.

Rule Mining Techniques to Predict Prokaryotic Metabolic Pathways

KAUST Repository

Saidi, Rabie

2017-08-28

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.

Rule Mining Techniques to Predict Prokaryotic Metabolic Pathways

KAUST Repository

Saidi, Rabie; Boudellioua, Imene; 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.

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

Science.gov (United States)

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.

Leveraging Bibliographic RDF Data for Keyword Prediction with Association Rule Mining (ARM

Directory of Open Access Journals (Sweden)

Nidhi Kushwaha

2014-11-01

Full Text Available The Semantic Web (Web 3.0 has been proposed as an efficient way to access the increasingly large amounts of data on the internet. The Linked Open Data Cloud project at present is the major effort to implement the concepts of the Seamtic Web, addressing the problems of inhomogeneity and large data volumes. RKBExplorer is one of many repositories implementing Open Data and contains considerable bibliographic information. This paper discusses bibliographic data, an important part of cloud data. Effective searching of bibiographic datasets can be a challenge as many of the papers residing in these databases do not have sufficient or comprehensive keyword information. In these cases however, a search engine based on RKBExplorer is only able to use information to retrieve papers based on author names and title of papers without keywords. In this paper we attempt to address this problem by using the data mining algorithm Association Rule Mining (ARM to develop keywords based on features retrieved from Resource Description Framework (RDF data within a bibliographic citation. We have demonstrate the applicability of this method for predicting missing keywords for bibliographic entries in several typical databases. −−−−− Paper presented at 1st International Symposium on Big Data and Cloud Computing Challenges (ISBCC-2014 March 27-28, 2014. Organized by VIT University, Chennai, India. Sponsored by BRNS.

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

Science.gov (United States)

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…

Dynamic association rules for gene expression data analysis.

Science.gov (United States)

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

Using the interestingness measure lift to generate association rules

OpenAIRE

Nada Hussein; Abdallah Alashqur; Bilal Sowan

2015-01-01

In this digital age, organizations have to deal with huge amounts of data, sometimes called Big Data. In recent years, the volume of data has increased substantially. Consequently, finding efficient and automated techniques for discovering useful patterns and relationships in the data becomes very important. In data mining, patterns and relationships can be represented in the form of association rules. Current techniques for discovering association rules rely on measures such as support for f...

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

Science.gov (United States)

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.

New game - new rules: mining in the democratic South Africa

Energy Technology Data Exchange (ETDEWEB)

Motlatsi, J. [National Union of Mineworkers (South Africa)

1995-12-31

Discusses the eight areas identified by the South African Union of Mineworkers as requiring new rules to improve safety and conditions in the South African mining industry. The areas are: improved health and safety; the elimination of racism; fair wages; decent living conditions; proper training; care for workers and areas affected by the downscaling of mining; development of an economically viable mining sector; and a mining sector run on a humane and participatory manner.

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

Science.gov (United States)

2010-01-01

... for Mine Safety and Health Administration employees. The rules in this section apply to employees of... Mine Safety and Health Act. Example: A mine inspector who was a former employee of mining company X... Secretary of labor for Mine Safety and Health or the Assistant Secretary's designee may grant an employee a...

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Using Machine Learning Methods Jointly to Find Better Set of Rules in Data Mining

Directory of Open Access Journals (Sweden)

SUG Hyontai

2017-01-01

Full Text Available Rough set-based data mining algorithms are one of widely accepted machine learning technologies because of their strong mathematical background and capability of finding optimal rules based on given data sets only without room for prejudiced views to be inserted on the data. But, because the algorithms find rules very precisely, we may confront with the overfitting problem. On the other hand, association rule algorithms find rules of association, where the association resides between sets of items in database. The algorithms find itemsets that occur more than given minimum support, so that they can find the itemsets practically in reasonable time even for very large databases by supplying the minimum support appropriately. In order to overcome the problem of the overfitting problem in rough set-based algorithms, first we find large itemsets, after that we select attributes that cover the large itemsets. By using the selected attributes only, we may find better set of rules based on rough set theory. Results from experiments support our suggested method.

RANCANG BANGUN SISTEM INFORMASI INVENTORI MENGGUNAKAN METODE ASSOCIATION RULES DI CV. DAMAR LANGIT

Directory of Open Access Journals (Sweden)

Zainul Fanani

2012-03-01

Kata Kunci : Sistem informasi, Inventori, Sistem Pendukung Keputusan (Decisition Support System, Association Rule, Data Mining, OLAP (Online Analitic Processing, Algoritma Apriori, Support, Confidence, Lift Rasio.

Quality prediction modeling for multistage manufacturing based on classification and association rule mining

Directory of Open Access Journals (Sweden)

Kao Hung-An

2017-01-01

Full Text Available For manufacturing enterprises, product quality is a key factor to assess production capability and increase their core competence. To reduce external failure cost, many research and methodology have been introduced in order to improve process yield rate, such as TQC/TQM, Shewhart CycleDeming's 14 Points, etc. Nowadays, impressive progress has been made in process monitoring and industrial data analysis because of the Industry 4.0 trend. Industries start to utilize quality control (QC methodology to lower inspection overhead and internal failure cost. Currently, the focus of QC is mostly in the inspection of single workstation and final product, however, for multistage manufacturing, many factors (like equipment, operators, parameters, etc. can have cumulative and interactive effects to the final quality. When failure occurs, it is difficult to resume the original settings for cause analysis. To address these problems, this research proposes a combination of principal components analysis (PCA with classification and association rule mining algorithms to extract features representing relationship of multiple workstations, predict final product quality, and analyze the root-cause of product defect. The method is demonstrated on a semiconductor data set.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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

2018-03-07

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.

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

Directory of Open Access Journals (Sweden)

Piontkivska Helen

2009-07-01

Full Text Available Abstract 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

Induction and pruning of classification rules for prediction of microseismic hazards in coal mines

Energy Technology Data Exchange (ETDEWEB)

Sikora, M. [Silesian Technical University, Gliwice (Poland)

2011-06-15

The paper presents results of application of a rule induction and pruning algorithm for classification of a microseismic hazard state in coal mines. Due to imbalanced distribution of examples describing states 'hazardous' and 'safe', the special algorithm was used for induction and rule pruning. The algorithm selects optimal parameters' values influencing rule induction and pruning based on training and tuning sets. A rule quality measure which decides about a form and classification abilities of rules that are induced is the basic parameter of the algorithm. The specificity and sensitivity of a classifier were used to evaluate its quality. Conducted tests show that the admitted method of rules induction and classifier's quality evaluation enables to get better results of classification of microseismic hazards than by methods currently used in mining practice. Results obtained by the rules-based classifier were also compared with results got by a decision tree induction algorithm and by a neuro-fuzzy system.

Social big data mining

CERN Document Server

Ishikawa, Hiroshi

2015-01-01

Social Media. Big Data and Social Data. Hypotheses in the Era of Big Data. Social Big Data Applications. Basic Concepts in Data Mining. Association Rule Mining. Clustering. Classification. Prediction. Web Structure Mining. Web Content Mining. Web Access Log Mining, Information Extraction and Deep Web Mining. Media Mining. Scalability and Outlier Detection.

Mining for associations between text and brain activation in a functional neuroimaging database

DEFF Research Database (Denmark)

Nielsen, Finn Årup; Hansen, Lars Kai; Balslev, D.

2004-01-01

We describe a method for mining a neuroimaging database for associations between text and brain locations. The objective is to discover association rules between words indicative of cognitive function as described in abstracts of neuroscience papers and sets of reported stereotactic Talairach...

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

Directory of Open Access Journals (Sweden)

Ujjwal Maulik

Full Text Available 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

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

Science.gov (United States)

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

Mining Views : database views for data mining

NARCIS (Netherlands)

Blockeel, H.; Calders, T.; Fromont, É.; Goethals, B.; Prado, A.; Nijssen, S.; De Raedt, L.

2007-01-01

We propose a relational database model towards the integration of data mining into relational database systems, based on the so called virtual mining views. We show that several types of patterns and models over the data, such as itemsets, association rules, decision trees and clusterings, can be

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

Science.gov (United States)

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

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

Directory of Open Access Journals (Sweden)

Lluís Sanmiquel

2018-03-01

Full Text Available 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.

Mining Views : database views for data mining

NARCIS (Netherlands)

Blockeel, H.; Calders, T.; Fromont, É.; Goethals, B.; Prado, A.

2008-01-01

We present a system towards the integration of data mining into relational databases. To this end, a relational database model is proposed, based on the so called virtual mining views. We show that several types of patterns and models over the data, such as itemsets, association rules and decision

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Association Rule Mining on Five Years of Motor Vehicle Crashes

Directory of Open Access Journals (Sweden)

Daher Jean Raymond

2016-01-01

Full Text Available Every year, road accidents kill more than a million people and injure more than 20 million worldwide. This paper aims to offer guidance on road safety and create awareness by pinpointing the major causes of traffic accidents. The study investigates motor vehicle crashes in the Genesee Finger Lakes Region of New York State. Frequency Pattern Growth algorithm is utilized to cultivate knowledge and create association rules to highlight the time and environment settings that cause the most catastrophic crashes. This knowledge can be used to warn drivers about the dangers of accidents, and how the consequences are worse given a specific context. For instance, a discovered rule from the data states that ‘most of the crashes occur between 12:00 pm and 6:00pm’; hence, it is suggested to modify existing navigation application to warn drivers about the increase in risk factor.

Association Rule Analysis for Tour Route Recommendation and Application to Wctsnop

Science.gov (United States)

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.

A New Approach of Multi-robot Cooperative Pursuit Based on Association Rule Data Mining

Directory of Open Access Journals (Sweden)

Jun Li

2010-02-01

Full Text Available An approach of cooperative hunting for multiple mobile targets by multi-robot is presented, which divides the pursuit process into forming the pursuit teams and capturing the targets. The data sets of attribute relationship is built by consulting all of factors about capturing evaders, then the interesting rules can be found by data mining from the data sets to build the pursuit teams. Through doping out the positions of targets, the pursuit game can be transformed into multi-robot path planning. Reinforcement learning is used to find the best path. The simulation results show that the mobile evaders can be captured effectively and efficiently, and prove the feasibility and validity of the given algorithm under a dynamic environment.

A New Approach of Multi-Robot Cooperative Pursuit Based on Association Rule Data Mining

Directory of Open Access Journals (Sweden)

Jun Li

2009-12-01

Full Text Available An approach of cooperative hunting for multiple mobile targets by multi-robot is presented, which divides the pursuit process into forming the pursuit teams and capturing the targets. The data sets of attribute relationship is built by consulting all of factors about capturing evaders, then the interesting rules can be found by data mining from the data sets to build the pursuit teams. Through doping out the positions of targets, the pursuit game can be transformed into multi-robot path planning. Reinforcement learning is used to find the best path. The simulation results show that the mobile evaders can be captured effectively and efficiently, and prove the feasibility and validity of the given algorithm under a dynamic environment.

Data mining theories, algorithms, and examples

CERN Document Server

Ye, Nong

2013-01-01

AN OVERVIEW OF DATA MINING METHODOLOGIESIntroduction to data mining methodologiesMETHODOLOGIES FOR MINING CLASSIFICATION AND PREDICTION PATTERNSRegression modelsBayes classifiersDecision treesMulti-layer feedforward artificial neural networksSupport vector machinesSupervised clusteringMETHODOLOGIES FOR MINING CLUSTERING AND ASSOCIATION PATTERNSHierarchical clusteringPartitional clusteringSelf-organized mapProbability distribution estimationAssociation rulesBayesian networksMETHODOLOGIES FOR MINING DATA REDUCTION PATTERNSPrincipal components analysisMulti-dimensional scalingLatent variable anal

ASSOCIATION RULE ANALYSIS FOR TOUR ROUTE RECOMMENDATION AND APPLICATION TO WCTSNOP

Directory of Open Access Journals (Sweden)

H. Fang

2017-09-01

Full Text Available 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.

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

Science.gov (United States)

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.

Association rule extraction from XML stream data for wireless sensor networks.

Science.gov (United States)

Paik, Juryon; Nam, Junghyun; Kim, Ung Mo; Won, Dongho

2014-07-18

With the advances of wireless sensor networks, they yield massive volumes of disparate, dynamic and geographically-distributed and heterogeneous data. The data mining community has attempted to extract knowledge from the huge amount of data that they generate. However, previous mining work in WSNs has focused on supporting simple relational data structures, like one table per network, while there is a need for more complex data structures. This deficiency motivates XML, which is the current de facto format for the data exchange and modeling of a wide variety of data sources over the web, to be used in WSNs in order to encourage the interchangeability of heterogeneous types of sensors and systems. However, mining XML data for WSNs has two challenging issues: one is the endless data flow; and the other is the complex tree structure. In this paper, we present several new definitions and techniques related to association rule mining over XML data streams in WSNs. To the best of our knowledge, this work provides the first approach to mining XML stream data that generates frequent tree items without any redundancy.

Process mining : overview and opportunities

NARCIS (Netherlands)

Aalst, van der W.M.P.

2012-01-01

Over the last decade, process mining emerged as a new research ¿eld that focuses on the analysis of processes using event data. Classical data mining techniques such as classi¿cation, clustering, regression, association rule learning, and sequence/episode mining do not focus on business process

Data mining and visualization techniques

Science.gov (United States)

Wong, Pak Chung [Richland, WA; Whitney, Paul [Richland, WA; Thomas, Jim [Richland, WA

2004-03-23

Disclosed are association rule identification and visualization methods, systems, and apparatus. An association rule in data mining is an implication of the form X.fwdarw.Y where X is a set of antecedent items and Y is the consequent item. A unique visualization technique that provides multiple antecedent, consequent, confidence, and support information is disclosed to facilitate better presentation of large quantities of complex association rules.

Collaborative Data Mining Tool for Education

Science.gov (United States)

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…

Association Rule Extraction from XML Stream Data for Wireless Sensor Networks

Science.gov (United States)

Paik, Juryon; Nam, Junghyun; Kim, Ung Mo; Won, Dongho

2014-01-01

With the advances of wireless sensor networks, they yield massive volumes of disparate, dynamic and geographically-distributed and heterogeneous data. The data mining community has attempted to extract knowledge from the huge amount of data that they generate. However, previous mining work in WSNs has focused on supporting simple relational data structures, like one table per network, while there is a need for more complex data structures. This deficiency motivates XML, which is the current de facto format for the data exchange and modeling of a wide variety of data sources over the web, to be used in WSNs in order to encourage the interchangeability of heterogeneous types of sensors and systems. However, mining XML data for WSNs has two challenging issues: one is the endless data flow; and the other is the complex tree structure. In this paper, we present several new definitions and techniques related to association rule mining over XML data streams in WSNs. To the best of our knowledge, this work provides the first approach to mining XML stream data that generates frequent tree items without any redundancy. PMID:25046017

Olap and data mining technologies' integration in the construction of interdimensional associative rules in multidimensional data

Directory of Open Access Journals (Sweden)

Микола Тихонович Фісун

2015-06-01

Full Text Available The features of associative rules in multidimensional data searching are presented in the article, specifically theoretical basis of association searching between different dimensions in OLAP cubes and formulas of their significance characteristics (support, confidence, lift, leverage calculation are shown. The method of interdimensional association rules generation is proposed. The implementation of this method as a component of operative and intellectual data analysis information system on database management system Caché platform is described.

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

Directory of Open Access Journals (Sweden)

Amir Hossein Azadnia

2013-01-01

Full Text Available 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.

Metal/nonmetal diesel particulate matter rule

Energy Technology Data Exchange (ETDEWEB)

Tomko, D.M. [United States Dept. of Labor, Mine Safety and Health Administration, Pittsburgh, PA (United States). Safety and Health Technology Center; Stackpole, R.P. [United States Dept. of Labor, Mine Safety and Health Administration, Triadelphia, WV (United States). Approval and Certification Center; Findlay, C.D. [United States Dept. of Labor, Mine Safety and Health Administration, Arlington, VA (United States). Metal/Nonmetal Safety and Health; Pomroy, W.H. [United States Dept. of Labor, Mine Safety and Health Administration, Duluth, MN (United States). Metal/Nonmetal North Central District

2010-07-01

The American Mine Safety and Health Administration (MSHA) issued a health standard in January 2001 designed to reduce exposure to diesel particulate matter (DPM) in underground metal and nonmetal mines. The rule established an interim concentration limit for DPM of 400 {mu}g/m{sup 3} of total carbon, to be followed in 2004 by a final limit of 160 {mu}g/m{sup 3} of total carbon. The 2001 rule was challenged in federal court by various mining trade associations and mining companies. The rule was subsequently amended. This paper highlighted the major provisions of the 2006 final rule and summarized MSHAs current compliance sampling procedures. The concentration limit was changed to a permissible exposure limit and the sampling surrogate was changed from total carbon to elemental carbon. The MSHA published a new rule in 2006 which based the final limit on a miner's personal exposure rather than a concentration limit. The final limit was phased in using 3 steps over 2 years. This paper also discussed engineering controls and a recent MSHA report on organic carbon, elemental carbon and total carbon emissions from a diesel engine fueled with various blends of standard diesel and biodiesel. In May 2008, about two-thirds of all underground metal/nonmetal mines achieved and maintained compliance with the rule. 20 refs.

Big data mining analysis method based on cloud computing

Science.gov (United States)

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.

An Incremental High-Utility Mining Algorithm with Transaction Insertion

Science.gov (United States)

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

An Incremental High-Utility Mining Algorithm with Transaction Insertion

Directory of Open Access Journals (Sweden)

Jerry Chun-Wei Lin

2015-01-01

Full Text Available 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.

Mine-associated wetlands as avian habitat

International Nuclear Information System (INIS)

Horstman, A.J.; Nawrot, J.R.; Woolf, A.

1998-01-01

Surveys for interior wetland birds at mine-associated emergent wetlands on coal surface mines in southern Illinois detected one state threatened and two state endangered species. Breeding by least bittern (Ixobrychus exilis) and common moorhen (Gallinula chloropus) was confirmed. Regional assessment of potential wetland bird habitat south of Illinois Interstate 64 identified a total of 8,109 ha of emergent stable water wetlands; 10% were associated with mining. Mine-associated wetlands with persistent hydrology and large expanses of emergent vegetation provide habitat that could potentially compensate for loss of natural wetlands in Illinois

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

Science.gov (United States)

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...

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

Science.gov (United States)

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.

Socioeconomic inequality of cancer mortality in the United States: a spatial data mining approach

Directory of Open Access Journals (Sweden)

Lam Nina SN

2006-02-01

Full Text Available Abstract Background The objective of this study was to demonstrate the use of an association rule mining approach to discover associations between selected socioeconomic variables and the four most leading causes of cancer mortality in the United States. An association rule mining algorithm was applied to extract associations between the 1988–1992 cancer mortality rates for colorectal, lung, breast, and prostate cancers defined at the Health Service Area level and selected socioeconomic variables from the 1990 United States census. Geographic information system technology was used to integrate these data which were defined at different spatial resolutions, and to visualize and analyze the results from the association rule mining process. Results Health Service Areas with high rates of low education, high unemployment, and low paying jobs were found to associate with higher rates of cancer mortality. Conclusion Association rule mining with geographic information technology helps reveal the spatial patterns of socioeconomic inequality in cancer mortality in the United States and identify regions that need further attention.

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

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

Gabriele Prati

Full Text Available 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.

Software tool for data mining and its applications

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Prediction of Metabolic Pathway Involvement in Prokaryotic UniProtKB Data by Association Rule Mining

KAUST Repository

Boudellioua, Imene; Saidi, Rabie; Hoehndorf, Robert; Martin, Maria J.; Solovyev, Victor

2016-01-01

The widening gap between known proteins and their functions has encouraged the development of methods to automatically infer annotations. Automatic functional annotation of proteins is expected to meet the conflicting requirements of maximizing annotation coverage, while minimizing erroneous functional assignments. This trade-off imposes a great challenge in designing intelligent systems to tackle the problem of automatic protein annotation. In this work, we present a system that utilizes rule mining techniques to predict metabolic pathways in prokaryotes. The resulting knowledge represents predictive models that assign pathway involvement to UniProtKB entries. We carried out an evaluation study of our system performance using cross-validation technique. We found that it achieved very promising results in pathway identification with an F1-measure of 0.982 and an AUC of 0.987. Our prediction models were then successfully applied to 6.2 million UniProtKB/TrEMBL reference proteome entries of prokaryotes. As a result, 663,724 entries were covered, where 436,510 of them lacked any previous pathway annotations.

Prediction of Metabolic Pathway Involvement in Prokaryotic UniProtKB Data by Association Rule Mining

KAUST Repository

Boudellioua, Imene

2016-07-08

The widening gap between known proteins and their functions has encouraged the development of methods to automatically infer annotations. Automatic functional annotation of proteins is expected to meet the conflicting requirements of maximizing annotation coverage, while minimizing erroneous functional assignments. This trade-off imposes a great challenge in designing intelligent systems to tackle the problem of automatic protein annotation. In this work, we present a system that utilizes rule mining techniques to predict metabolic pathways in prokaryotes. The resulting knowledge represents predictive models that assign pathway involvement to UniProtKB entries. We carried out an evaluation study of our system performance using cross-validation technique. We found that it achieved very promising results in pathway identification with an F1-measure of 0.982 and an AUC of 0.987. Our prediction models were then successfully applied to 6.2 million UniProtKB/TrEMBL reference proteome entries of prokaryotes. As a result, 663,724 entries were covered, where 436,510 of them lacked any previous pathway annotations.

Utilization of BSS-Safety Series 115 in facilities for mining ore with uranium and thorium associated to it

International Nuclear Information System (INIS)

Matta, Luiz Ernesto S. de C.; Ferreira, Paulo R. Rocha; Mouco, Charles D. do C.L.

1999-01-01

During the year of 1995, the Brazilian Nuclear Energy Commission, started a investigation program called Mining Project. The main objective of this program was to create a integrated study of environmental and occupational conditions of mining ores with uranium and thorium associated to it. Several technical visits were done at four different types of mines: coal, phosphate, niobium and gold. This work presents the area monitoring results obtained by the occupational radiation protection group. The data found in these inspections were compared to limits established by national rules. A special assessment of the data obtained was done for a future adoption of the BSS-Safety Series 115 recommendations. (author)

Mining Long, Sharable Patterns in Trajectories of Moving Objects

DEFF Research Database (Denmark)

Gidofalvi, Gyozo; Pedersen, Torben Bach

2009-01-01

The efficient analysis of spatio-temporal data, generated by moving objects, is an essential requirement for intelligent location-based services. Spatio-temporal rules can be found by constructing spatio-temporal baskets, from which traditional association rule mining methods can discover spatio...

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

Science.gov (United States)

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.

Application of an empiric Bayesian data mining algorithm to reports of pancreatitis associated with atypical antipsychotics.

Science.gov (United States)

Hauben, Manfred

2004-09-01

To compare the results from one frequently cited data mining algorithm with those from a study, which was published in a peer-reviewed journal, that examined the association of pancreatitis with selected atypical antipsychotics observed by traditional rule-based methods of signal detection. Retrospective pharmacovigilance study. The widely studied data mining algorithm known as the Multi-item Gamma Poisson Shrinker (MGPS) was applied to adverse-event reports from the United States Food and Drug Administration's Adverse Event Reporting System database through the first quarter of 2003 for clozapine, olanzapine, and risperidone to determine if a significant signal of pancreatitis would have been generated by this method in advance of their review or the addition of these events to the respective product labels. Data mining was performed by using nine preferred terms relevant to drug-induced pancreatitis from the Medical Dictionary for Regulatory Activities (MedDRA). Results from a previous study on the antipsychotics were reviewed and analyzed. Physicians' Desk References (PDRs) starting from 1994 were manually reviewed to determine the first year that pancreatitis was listed as an adverse event in the product label for each antipsychotic. This information was used as a surrogate marker of the timing of initial signal detection by traditional criteria. Pancreatitis was listed as an adverse event in a PDR for all three atypical antipsychotics. Despite the presence of up to 88 reports/drug-event combination in the Food and Drug Administration's Adverse Event Reporting System database, the MGPS failed to generate a signal of disproportional reporting of pancreatitis associated with the three antipsychotics despite the signaling of these drug-event combinations by traditional rule-based methods, as reflected in product labeling and/or the literature. These discordant findings illustrate key principles in the application of data mining algorithms to drug safety

Mining and sustainable development: environmental policies and programmes of mining industry associations

International Nuclear Information System (INIS)

Miller, C.G.

1997-01-01

Mining industry policies and practices have evolved rapidly in the environmental area, and more recently in the social area as well. Mining industry associations are using a variety of methods to stimulate and assist their member companies as they improve their environmental, social and economic performance. These associations provide opportunities for companies to use collaborative approaches in developing and applying improved technology, systems and practices (author)

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

Science.gov (United States)

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...

Mining Educational Data to Analyze the Student Motivation Behavior

OpenAIRE

Kunyanuth Kularbphettong; Cholticha Tongsiri

2012-01-01

The purpose of this research aims to discover the knowledge for analysis student motivation behavior on e-Learning based on Data Mining Techniques, in case of the Information Technology for Communication and Learning Course at Suan Sunandha Rajabhat University. The data mining techniques was applied in this research including association rules, classification techniques. The results showed that using data mining technique can indicate the important variables that influenc...

Research of the Occupational Psychological Impact Factors Based on the Frequent Item Mining of the Transactional Database

Directory of Open Access Journals (Sweden)

Cheng Dongmei

2015-01-01

Full Text Available Based on the massive reading of data mining and association rules mining documents, this paper will start from compressing transactional database and propose the frequent complementary item storage structure of the transactional database. According to the previous analysis, this paper will also study the association rules mining algorithm based on the frequent complementary item storage structure of the transactional database. At last, this paper will apply this mining algorithm in the test results analysis module of team psychological health assessment system, and will extract the relationship between each psychological impact factor, so as to provide certain guidance for psychologists in their mental illness treatment.

Studying Co-evolution of Production and Test Code Using Association Rule Mining

NARCIS (Netherlands)

Lubsen, Z.; Zaidman, A.; Pinzger, M.

2009-01-01

Long version of the short paper accepted for publication in the proceedings of the 6th International Working Conference on Mining Software Repositories (MSR 2009). Unit tests are generally acknowledged as an important aid to produce high quality code, as they provide quick feedback to developers on

On construction of partial association rules

KAUST Repository

Moshkov, Mikhail

2009-01-01

This paper is devoted to the study of approximate algorithms for minimization of partial association rule length. It is shown that under some natural assumptions on the class NP, a greedy algorithm is close to the best polynomial approximate algorithms for solving of this NP-hard problem. The paper contains various bounds on precision of the greedy algorithm, bounds on minimal length of rules based on an information obtained during greedy algorithm work, and results of the study of association rules for the most part of binary information systems. © 2009 Springer Berlin Heidelberg.

Mining Branching Rules from Past Survey Data with an Illustration Using a Geriatric Assessment Survey for Older Adults with Cancer

Directory of Open Access Journals (Sweden)

Daniel R. Jeske

2016-05-01

Full Text Available We construct a fast data mining algorithm that can be used to identify high-frequency response patterns in historical surveys. Identification of these patterns leads to the derivation of question branching rules that shorten the time required to complete a survey. The data mining algorithm allows the user to control the error rate that is incurred through the use of implied answers that go along with each branching rule. The context considered is binary response questions, which can be obtained from multi-level response questions through dichotomization. The algorithm is illustrated by the analysis of four sections of a geriatric assessment survey used by oncologists. Reductions in the number of questions that need to be asked in these four sections range from 33% to 54%.

A Novel Texture Classification Procedure by using Association Rules

Directory of Open Access Journals (Sweden)

L. Jaba Sheela

2008-11-01

Full Text Available Texture can be defined as a local statistical pattern of texture primitives in observer’s domain of interest. Texture classification aims to assign texture labels to unknown textures, according to training samples and classification rules. Association rules have been used in various applications during the past decades. Association rules capture both structural and statistical information, and automatically identify the structures that occur most frequently and relationships that have significant discriminative power. So, association rules can be adapted to capture frequently occurring local structures in textures. This paper describes the usage of association rules for texture classification problem. The performed experimental studies show the effectiveness of the association rules. The overall success rate is about 98%.

Application of data mining techniques for nuclear data and instrumentation

International Nuclear Information System (INIS)

Toshniwal, Durga

2013-01-01

Data mining is defined as the discovery of previously unknown, valid, novel, potentially useful, and understandable patterns in large databases. It encompasses many different techniques and algorithms which differ in the kinds of data that can be analyzed and the form of knowledge representation used to convey the discovered knowledge. Patterns in the data can be represented in many different forms, including classification rules, association rules, clusters, etc. Data mining thus deals with the discovery of hidden trends and patterns from large quantities of data. The field of data mining is emerging as a new, fundamental research area with important applications to science, engineering, medicine, business, and education. It is an interdisciplinary research area and draws upon several roots, including database systems, machine learning, information systems, statistics and expert systems. Data mining, when performed on time series data, is known as time series data mining (TSDM). A time series is a sequence of real numbers, each number representing a value at a point of time. During the past few years, there has been an explosion of research in the area of time series data mining. This includes attempts to model time series data, to design languages to query such data, and to develop access structures to efficiently process queries on such data. Time series data arises naturally in many real-world applications. Efficient discovery of knowledge through time series data mining can be helpful in several domains such as: Stock market analysis, Weather forecasting etc. An important application area of data mining techniques is in nuclear power plant and related data. Nuclear power plant data can be represented in form of time sequences. Often it may be of prime importance to analyze such data to find trends and anomalies. The general goals of data mining include feature extraction, similarity search, clustering and classification, association rule mining and anomaly

Dynamic Programming Approach for Construction of Association Rule Systems

KAUST Repository

Alsolami, Fawaz

2016-11-18

In the paper, an application of dynamic programming approach for optimization of association rules from the point of view of knowledge representation is considered. The association rule set is optimized in two stages, first for minimum cardinality and then for minimum length of rules. Experimental results present cardinality of the set of association rules constructed for information system and lower bound on minimum possible cardinality of rule set based on the information obtained during algorithm work as well as obtained results for length.

Dynamic Programming Approach for Construction of Association Rule Systems

KAUST Repository

Alsolami, Fawaz; Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

2016-01-01

In the paper, an application of dynamic programming approach for optimization of association rules from the point of view of knowledge representation is considered. The association rule set is optimized in two stages, first for minimum cardinality and then for minimum length of rules. Experimental results present cardinality of the set of association rules constructed for information system and lower bound on minimum possible cardinality of rule set based on the information obtained during algorithm work as well as obtained results for length.

Data mining methods

CERN Document Server

Chattamvelli, Rajan

2015-01-01

DATA MINING METHODS, Second Edition discusses both theoretical foundation and practical applications of datamining in a web field including banking, e-commerce, medicine, engineering and management. This book starts byintroducing data and information, basic data type, data category and applications of data mining. The second chapterbriefly reviews data visualization technology and importance in data mining. Fundamentals of probability and statisticsare discussed in chapter 3, and novel algorithm for sample covariants are derived. The next two chapters give an indepthand useful discussion of data warehousing and OLAP. Decision trees are clearly explained and a new tabularmethod for decision tree building is discussed. The chapter on association rules discusses popular algorithms andcompares various algorithms in summary table form. An interesting application of genetic algorithm is introduced inthe next chapter. Foundations of neural networks are built from scratch and the back propagation algorithm is derived...

Mining Staff Assignment Rules from Event-Based Data

NARCIS (Netherlands)

Ly, Linh Thao; Rinderle, Stefanie; Dadam, Peter; Reichert, Manfred; Bussler, Christoph J.; Haller, Armin

2006-01-01

Process mining offers methods and techniques for capturing process behaviour from log data of past process executions. Although many promising approaches on mining the control flow have been published, no attempt has been made to mine the staff assignment situation of business processes. In this

A fuzzy hill-climbing algorithm for the development of a compact associative classifier

Science.gov (United States)

Mitra, Soumyaroop; Lam, Sarah S.

2012-02-01

Classification, a data mining technique, has widespread applications including medical diagnosis, targeted marketing, and others. Knowledge discovery from databases in the form of association rules is one of the important data mining tasks. An integrated approach, classification based on association rules, has drawn the attention of the data mining community over the last decade. While attention has been mainly focused on increasing classifier accuracies, not much efforts have been devoted towards building interpretable and less complex models. This paper discusses the development of a compact associative classification model using a hill-climbing approach and fuzzy sets. The proposed methodology builds the rule-base by selecting rules which contribute towards increasing training accuracy, thus balancing classification accuracy with the number of classification association rules. The results indicated that the proposed associative classification model can achieve competitive accuracies on benchmark datasets with continuous attributes and lend better interpretability, when compared with other rule-based systems.

Exploring the potential of data mining techniques for the analysis of accident patterns

DEFF Research Database (Denmark)

Prato, Carlo Giacomo; Bekhor, Shlomo; Galtzur, Ayelet

2010-01-01

Research in road safety faces major challenges: individuation of the most significant determinants of traffic accidents, recognition of the most recurrent accident patterns, and allocation of resources necessary to address the most relevant issues. This paper intends to comprehend which data mining...... and association rules) data mining techniques are implemented for the analysis of traffic accidents occurred in Israel between 2001 and 2004. Results show that descriptive techniques are useful to classify the large amount of analyzed accidents, even though introduce problems with respect to the clear...... importance of input and intermediate neurons, and the relative importance of hundreds of association rules. Further research should investigate whether limiting the analysis to fatal accidents would simplify the task of data mining techniques in recognizing accident patterns without the “noise” probably...

Finding Exception For Association Rules Via SQL Queries

Directory of Open Access Journals (Sweden)

Luminita DUMITRIU

2000-12-01

Full Text Available Finding association rules is mainly based on generating larger and larger frequent set candidates, starting from frequent attributes in the database. The frequent sets can be organised as a part of a lattice of concepts according to the Formal Concept Analysis approach. Since the lattice construction is database contents-dependent, the pseudo-intents (see Formal Concept Analysis are avoided. Association rules between concept intents (closed sets A=>B are partial implication rules, meaning that there is some data supporting A and (not B; fully explaining the data requires finding exceptions for the association rules. The approach applies to Oracle databases, via SQL queries.

Applied data mining for business and industry

CERN Document Server

Giudici, Paolo

2009-01-01

The increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract knowledge from such data. This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications. Introduces data mining methods and applications.Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods.Includes many recent developments such as association and sequence rules, graphical Markov models, lifetime value modelling, credit risk, operational risk and web mining.Features detailed case studies based on applied projects within industry.Incorporates discussion of data mining software, with case studies a...

Compass: A hybrid method for clinical and biobank data mining

DEFF Research Database (Denmark)

Krysiak-Baltyn, Konrad; Petersen, Thomas Nordahl; Audouze, Karine Marie Laure

2014-01-01

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...

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

Science.gov (United States)

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

Directory of Open Access Journals (Sweden)

Yu-Lung Hsieh

2014-01-01

Full Text Available 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.

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

Science.gov (United States)

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...

DISEASES: text mining and data integration of disease-gene associations.

Science.gov (United States)

Pletscher-Frankild, Sune; Pallejà, Albert; Tsafou, Kalliopi; Binder, Janos X; Jensen, Lars Juhl

2015-03-01

Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease-gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for named entity recognition of human genes and diseases, which we combine with a scoring scheme that takes into account co-occurrences both within and between sentences. We show that this approach is able to extract half of all manually curated associations with a false positive rate of only 0.16%. Nonetheless, text mining should not stand alone, but be combined with other types of evidence. For this reason, we have developed the DISEASES resource, which integrates the results from text mining with manually curated disease-gene associations, cancer mutation data, and genome-wide association studies from existing databases. The DISEASES resource is accessible through a web interface at http://diseases.jensenlab.org/, where the text-mining software and all associations are also freely available for download. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

An Evolutionary Algorithm to Mine High-Utility Itemsets

Directory of Open Access Journals (Sweden)

Jerry Chun-Wei Lin

2015-01-01

Full Text Available High-utility itemset mining (HUIM is a critical issue in recent years since it can be used to reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM of association rules (ARs. In this paper, an evolutionary algorithm is presented to efficiently mine high-utility itemsets (HUIs based on the binary particle swarm optimization. A maximal pattern (MP-tree strcutrue is further designed to solve the combinational problem in the evolution process. Substantial experiments on real-life datasets show that the proposed binary PSO-based algorithm has better results compared to the state-of-the-art GA-based algorithm.

A Template Model for Multidimensional Inter-Transactional Association Rules

NARCIS (Netherlands)

Feng, L.; Yu, J.X.; Lu, H.J.; Han, J.W.

2002-01-01

Multidimensional inter-transactional association rules extend the traditional association rules to describe more general associations among items with multiple properties across transactions. “After McDonald and Burger King open branches, KFC will open a branch two months later and one mile away��?

AN EFFICIENT DATA MINING METHOD TO FIND FREQUENT ITEM SETS IN LARGE DATABASE USING TR- FCTM

Directory of Open Access Journals (Sweden)

Saravanan Suba

2016-01-01

Full Text Available Mining association rules in large database is one of most popular data mining techniques for business decision makers. Discovering frequent item set is the core process in association rule mining. Numerous algorithms are available in the literature to find frequent patterns. Apriori and FP-tree are the most common methods for finding frequent items. Apriori finds significant frequent items using candidate generation with more number of data base scans. FP-tree uses two database scans to find significant frequent items without using candidate generation. This proposed TR-FCTM (Transaction Reduction- Frequency Count Table Method discovers significant frequent items by generating full candidates once to form frequency count table with one database scan. Experimental results of TR-FCTM shows that this algorithm outperforms than Apriori and FP-tree.

Greedy algorithms withweights for construction of partial association rules

KAUST Repository

Moshkov, Mikhail; Piliszczu, Marcin; Zielosko, Beata Marta

2009-01-01

This paper is devoted to the study of approximate algorithms for minimization of the total weight of attributes occurring in partial association rules. We consider mainly greedy algorithms with weights for construction of rules. The paper contains bounds on precision of these algorithms and bounds on the minimal weight of partial association rules based on an information obtained during the greedy algorithm run.

Greedy algorithms withweights for construction of partial association rules

KAUST Repository

Moshkov, Mikhail

2009-09-10

This paper is devoted to the study of approximate algorithms for minimization of the total weight of attributes occurring in partial association rules. We consider mainly greedy algorithms with weights for construction of rules. The paper contains bounds on precision of these algorithms and bounds on the minimal weight of partial association rules based on an information obtained during the greedy algorithm run.

Spatio-Temporal Pattern Mining on Trajectory Data Using Arm

Science.gov (United States)

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.

Personalized privacy-preserving frequent itemset mining using randomized response.

Science.gov (United States)

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.

Hospitalization patterns associated with Appalachian coal mining.

Science.gov (United States)

Hendryx, Michael; Ahern, Melissa M; Nurkiewicz, Timothy R

2007-12-01

The goal of this study was to test whether the volume of coal mining was related to population hospitalization risk for diseases postulated to be sensitive or insensitive to coal mining by-products. The study was a retrospective analysis of 2001 adult hospitalization data (n = 93,952) for West Virginia, Kentucky, and Pennsylvania, merged with county-level coal production figures. Hospitalization data were obtained from the Health Care Utilization Project National Inpatient Sample. Diagnoses postulated to be sensitive to coal mining by-product exposure were contrasted with diagnoses postulated to be insensitive to exposure. Data were analyzed using hierarchical nonlinear models, controlling for patient age, gender, insurance, comorbidities, hospital teaching status, county poverty, and county social capital. Controlling for covariates, the volume of coal mining was significantly related to hospitalization risk for two conditions postulated to be sensitive to exposure: hypertension and chronic obstructive pulmonary disease (COPD). The odds for a COPD hospitalization increased 1% for each 1462 tons of coal, and the odds for a hypertension hospitalization increased 1% for each 1873 tons of coal. Other conditions were not related to mining volume. Exposure to particulates or other pollutants generated by coal mining activities may be linked to increased risk of COPD and hypertension hospitalizations. Limitations in the data likely result in an underestimate of associations.

Empirical approach for designing of support system in mechanized coal pillar mining

Energy Technology Data Exchange (ETDEWEB)

Kushwaha, A.; Singh, S.K.; Tewari, S.; Sinha, A. [Central Institute of Mining & Fuel Research, Dhanbad (India)

2010-10-15

Mechanized room-and-pillar system of coal pillar mining using side dump loading machine or load haul dumper machine, or by continuous miner, is the presently most dominant under ground method of extraction in India. Under this method of extraction, strata control is a major problem affecting safety and productivity of the mine. As per existing Director General of Mine Safety guidelines, systematic support rules must be followed at the depillaring faces irrespective of immediate roof rock type and competency. Therefore, there is a high chance that sometimes these systematic support rules give unnecessarily high support, or sometimes inadequate support, which may lead to roof failure at the face. As a result, there is a big loss of life and material including coal in terms of left-outribs/stooks and other associated mining equipment deployed at the faces. Therefore, in the present paper, authors attempted to develop generalized empirical equations for estimating the required support load density at different places of the face based on geotechnical parameters of the mine and physico-mechanical properties of the immediate roof rocks for designing of support system during mechanized coal pillar mining.

75 FR 22723 - Stream Protection Rule; Environmental Impact Statement

Science.gov (United States)

2010-04-30

..., 784, 816, and 817 RIN 1029-AC63 Stream Protection Rule; Environmental Impact Statement AGENCY: Office... streams from the adverse impacts of surface coal mining operations. We are requesting comments for the... mining activities may be conducted in or near perennial or intermittent streams. That rule, which this...

Application of text mining for customer evaluations in commercial banking

Science.gov (United States)

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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

A study of trends in occupational risks associated with coal mining

International Nuclear Information System (INIS)

Amoudru, C.

1980-01-01

The coal industry is well known as a major source of specific types of risk and harmful effects including, for instance, harm to the environment, pollution from various surface installations and hazards associated with the actual task of mining. We shall confine our attention to the third group and discuss only the occupational risks facing miners and ex-miners. Unlike the nuclear and oil industries, coal-mines employ very large work-forces, and the risks associated with mining therefore have a considerable impact. Mining is also a highly integrated industry: a mine's own work-force carries out all the underground engineering work (preparatory excavations, installation work, etc.) as well as maintenance. In this narrow field, a distinction should immediately be drawn between two main areas: industrial accidents; and occupational diseases, which include silicosis or, more precisely, coal-miner's pneumoconiosis

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

Science.gov (United States)

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.

Event metadata records as a testbed for scalable data mining

International Nuclear Information System (INIS)

Gemmeren, P van; Malon, D

2010-01-01

At a data rate of 200 hertz, event metadata records ('TAGs,' in ATLAS parlance) provide fertile grounds for development and evaluation of tools for scalable data mining. It is easy, of course, to apply HEP-specific selection or classification rules to event records and to label such an exercise 'data mining,' but our interest is different. Advanced statistical methods and tools such as classification, association rule mining, and cluster analysis are common outside the high energy physics community. These tools can prove useful, not for discovery physics, but for learning about our data, our detector, and our software. A fixed and relatively simple schema makes TAG export to other storage technologies such as HDF5 straightforward. This simplifies the task of exploiting very-large-scale parallel platforms such as Argonne National Laboratory's BlueGene/P, currently the largest supercomputer in the world for open science, in the development of scalable tools for data mining. Using a domain-neutral scientific data format may also enable us to take advantage of existing data mining components from other communities. There is, further, a substantial literature on the topic of one-pass algorithms and stream mining techniques, and such tools may be inserted naturally at various points in the event data processing and distribution chain. This paper describes early experience with event metadata records from ATLAS simulation and commissioning as a testbed for scalable data mining tool development and evaluation.

SPATIO-TEMPORAL PATTERN MINING ON TRAJECTORY DATA USING ARM

Directory of Open Access Journals (Sweden)

S. Khoshahval

2017-09-01

Full Text Available 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.

PM2 : a Process Mining Project Methodology

NARCIS (Netherlands)

Eck, van M.L.; Lu, X.; Leemans, S.J.J.; Aalst, van der W.M.P.; Zdravkovic, J.; Kirikova, M.; Johannesson, P.

2015-01-01

Process mining aims to transform event data recorded in information systems into knowledge of an organisation’s business processes. The results of process mining analysis can be used to improve process performance or compliance to rules and regulations. However, applying process mining in practice

Is there an association of circulatory hospitalizations independent of mining employment in coal-mining and non-coal-mining counties in west virginia?

Science.gov (United States)

Talbott, Evelyn O; Sharma, Ravi K; Buchanich, Jeanine; Stacy, Shaina L

2015-04-01

Exposures associated with coal mining activities, including diesel fuel exhaust, products used in coal processing, and heavy metals and other forms of particulate matter, may impact the health of nearby residents. We investigated the relationships between county-level circulatory hospitalization rates (CHRs) in coal and non-coal-mining communities of West Virginia, coal production, coal employment, and sociodemographic factors. Direct age-adjusted CHRs were calculated using West Virginia hospitalizations from 2005 to 2009. Spatial regressions were conducted to explore associations between CHR and total, underground, and surface coal production. After adjustment, neither total, nor surface, nor underground coal production was significantly related to rate of hospitalization for circulatory disease. Our findings underscore the significant role sociodemographic and behavioral factors play in the health and well-being of coal mining communities.

Formal and Computational Properties of the Confidence Boost of Association Rules

OpenAIRE

Balcázar, José L.

2011-01-01

Some existing notions of redundancy among association rules allow for a logical-style characterization and lead to irredundant bases of absolutely minimum size. One can push the intuition of redundancy further and find an intuitive notion of interest of an association rule, in terms of its "novelty" with respect to other rules. Namely: an irredundant rule is so because its confidence is higher than what the rest of the rules would suggest; then, one can ask: how much higher? We propose to mea...

Determination of Relations between Systolic Blood Pressure and Heart Attack in Patients with Type 2 Diabetes with Association Rules

Directory of Open Access Journals (Sweden)

Seyyed Payam Shariatpanahi

2018-03-01

Full Text Available Abstract Background: Today, the high prevalence of diabetes and its complications are one of the most important public health issues worldwide. For this reason, finding relations between diabetes risk factors is very effective in preventing and reducing complications. For discovering these relations, the data mining methods can be used. By extracting association rules, which is one of the data mining techniques, we can discover the relations between a large numbers of variables in a disease. Materials and Methods: The population of this study was 1046 patients with type 2 diabetes, whose data had recorded between 2011 and 2014 at the Special Clinic for Diabetes in Tehran's Imam Khomeini Hospital. After pre-processing step with SPSS19 software, 573 people entered the analysis phase. The FP-Growth algorithm was applied to the data set to discover the relations between heart attack and other risk factors using Rapid miner5 software. Relations, after extraction, were given to the doctor to confirm clinical validation. Results: The obtained results of studying these 573 people (Including 292 (51% women and 281 (49% men, with age range 27 to 82 years showed that the lack of blood pressure, creatinine and diastolic blood pressure at its normal level, despite higher systolic blood pressure level than normal, doesn't increase the probability of heart attack. Conclusion: Using association rules is a good way of identifying relations between the risk factors of a disease. Also, it can provide new hypotheses to do epidemiological studies for researchers.

Locating previously unknown patterns in data-mining results: a dual data- and knowledge-mining method

Directory of Open Access Journals (Sweden)

Knaus William A

2006-03-01

Full Text Available Abstract Background Data mining can be utilized to automate analysis of substantial amounts of data produced in many organizations. However, data mining produces large numbers of rules and patterns, many of which are not useful. Existing methods for pruning uninteresting patterns have only begun to automate the knowledge acquisition step (which is required for subjective measures of interestingness, hence leaving a serious bottleneck. In this paper we propose a method for automatically acquiring knowledge to shorten the pattern list by locating the novel and interesting ones. Methods The dual-mining method is based on automatically comparing the strength of patterns mined from a database with the strength of equivalent patterns mined from a relevant knowledgebase. When these two estimates of pattern strength do not match, a high "surprise score" is assigned to the pattern, identifying the pattern as potentially interesting. The surprise score captures the degree of novelty or interestingness of the mined pattern. In addition, we show how to compute p values for each surprise score, thus filtering out noise and attaching statistical significance. Results We have implemented the dual-mining method using scripts written in Perl and R. We applied the method to a large patient database and a biomedical literature citation knowledgebase. The system estimated association scores for 50,000 patterns, composed of disease entities and lab results, by querying the database and the knowledgebase. It then computed the surprise scores by comparing the pairs of association scores. Finally, the system estimated statistical significance of the scores. Conclusion The dual-mining method eliminates more than 90% of patterns with strong associations, thus identifying them as uninteresting. We found that the pruning of patterns using the surprise score matched the biomedical evidence in the 100 cases that were examined by hand. The method automates the acquisition of

Legal aspects of search and mining of nuclear ores under Brazilian law

International Nuclear Information System (INIS)

Godinho, T.M.

1980-06-01

The legal aspects of mining in the Brazilian law its general principles, the basic concepts and rules established in the constitution of Brazil, in the mining code and in special laws are analysed. The rules for mining and usage of nuclear ores and other ores of interest to the nuclear field are emphasized. (A.L.) [pt

Hazards associated with stage one-mining

International Nuclear Information System (INIS)

Anon.

1975-01-01

Radiation hazards in uranium mining arise from the presence of radon-222, a gas which can escape from exposed rock surfaces into the air. Radon daughter products have been associated with an increased incidence of respiratory lung cancer. Other hazards include the tailings which arise from the extraction of uranium ores. The tailings still contain most of the original radium and emit gamma rays and radon gas. The hazards associated with uranium enrichment and fuel manufacture are also discussed. (R.L.)

Quantifying Associations between Environmental Stressors and Demographic Factors

Science.gov (United States)

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...

On construction of partial association rules

KAUST Repository

Moshkov, Mikhail; Piliszczuk, Marcin; Zielosko, Beata Marta

2009-01-01

This paper is devoted to the study of approximate algorithms for minimization of partial association rule length. It is shown that under some natural assumptions on the class NP, a greedy algorithm is close to the best polynomial approximate

78 FR 48591 - Refuge Alternatives for Underground Coal Mines

Science.gov (United States)

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...

Improving diagnostic accuracy using agent-based distributed data mining system.

Science.gov (United States)

Sridhar, S

2013-09-01

The use of data mining techniques to improve the diagnostic system accuracy is investigated in this paper. The data mining algorithms aim to discover patterns and extract useful knowledge from facts recorded in databases. Generally, the expert systems are constructed for automating diagnostic procedures. The learning component uses the data mining algorithms to extract the expert system rules from the database automatically. Learning algorithms can assist the clinicians in extracting knowledge automatically. As the number and variety of data sources is dramatically increasing, another way to acquire knowledge from databases is to apply various data mining algorithms that extract knowledge from data. As data sets are inherently distributed, the distributed system uses agents to transport the trained classifiers and uses meta learning to combine the knowledge. Commonsense reasoning is also used in association with distributed data mining to obtain better results. Combining human expert knowledge and data mining knowledge improves the performance of the diagnostic system. This work suggests a framework of combining the human knowledge and knowledge gained by better data mining algorithms on a renal and gallstone data set.

VICKEY: Mining Conditional Keys on Knowledge Bases

DEFF Research Database (Denmark)

Symeonidou, Danai; Prado, Luis Antonio Galarraga Del; Pernelle, Nathalie

2017-01-01

A conditional key is a key constraint that is valid in only a part of the data. In this paper, we show how such keys can be mined automatically on large knowledge bases (KBs). For this, we combine techniques from key mining with techniques from rule mining. We show that our method can scale to KBs...

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

Science.gov (United States)

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.

DrugQuest - a text mining workflow for drug association discovery.

Science.gov (United States)

Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Theodosiou, Theodosios; Vizirianakis, Ioannis S; Iliopoulos, Ioannis

2016-06-06

Text mining and data integration methods are gaining ground in the field of health sciences due to the exponential growth of bio-medical literature and information stored in biological databases. While such methods mostly try to extract bioentity associations from PubMed, very few of them are dedicated in mining other types of repositories such as chemical databases. Herein, we apply a text mining approach on the DrugBank database in order to explore drug associations based on the DrugBank "Description", "Indication", "Pharmacodynamics" and "Mechanism of Action" text fields. We apply Name Entity Recognition (NER) techniques on these fields to identify chemicals, proteins, genes, pathways, diseases, and we utilize the TextQuest algorithm to find additional biologically significant words. Using a plethora of similarity and partitional clustering techniques, we group the DrugBank records based on their common terms and investigate possible scenarios why these records are clustered together. Different views such as clustered chemicals based on their textual information, tag clouds consisting of Significant Terms along with the terms that were used for clustering are delivered to the user through a user-friendly web interface. DrugQuest is a text mining tool for knowledge discovery: it is designed to cluster DrugBank records based on text attributes in order to find new associations between drugs. The service is freely available at http://bioinformatics.med.uoc.gr/drugquest .

A procedure for NEPA assessment of selenium hazards associated with mining.

Science.gov (United States)

Lemly, A Dennis

2007-02-01

This paper gives step-by-step instructions for assessing aquatic selenium hazards associated with mining. The procedure was developed to provide the U.S. Forest Service with a proactive capability for determining the risk of selenium pollution when it reviews mine permit applications in accordance with the National Environmental Policy Act (NEPA). The procedural framework is constructed in a decision-tree format in order to guide users through the various steps, provide a logical sequence for completing individual tasks, and identify key decision points. There are five major components designed to gather information on operational parameters of the proposed mine as well as key aspects of the physical, chemical, and biological environment surrounding it--geological assessment, mine operation assessment, hydrological assessment, biological assessment, and hazard assessment. Validation tests conducted at three mines where selenium pollution has occurred confirmed that the procedure will accurately predict ecological risks. In each case, it correctly identified and quantified selenium hazard, and indicated the steps needed to reduce this hazard to an acceptable level. By utilizing the procedure, NEPA workers can be confident in their ability to understand the risk of aquatic selenium pollution and take appropriate action. Although the procedure was developed for the Forest Service it should also be useful to other federal land management agencies that conduct NEPA assessments, as well as regulatory agencies responsible for issuing coal mining permits under the authority of the Surface Mining Control and Reclamation Act (SMCRA) and associated Section 401 water quality certification under the Clean Water Act. Mining companies will also benefit from the application of this procedure because priority selenium sources can be identified in relation to specific mine operating parameters. The procedure will reveal the point(s) at which there is a need to modify operating

Law 19.126. It dictate Regulatory standards about Mining of great bearing

International Nuclear Information System (INIS)

2013-01-01

It statute rules for regulating mining projects of great size, ownership, location, related mining activities, mine closure plan, exploitation concession contract, taxation regime, canon, infractions and sanctions

State Identification of Hoisting Motors Based on Association Rules for Quayside Container Crane

Science.gov (United States)

Li, Q. Z.; Gang, T.; Pan, H. Y.; Xiong, H.

2017-07-01

Quay container crane hoisting motor is a complex system, and the characteristics of long-term evolution and change of running status of there is a rule, and use it. Through association rules analysis, this paper introduced the similarity in association rules, and quay container crane hoisting motor status identification. Finally validated by an example, some rules change amplitude is small, regular monitoring, not easy to find, but it is precisely because of these small changes led to mechanical failure. Therefore, using the association rules change in monitoring the motor status has the very strong practical significance.

76 FR 81761 - Mine Safety Disclosure

Science.gov (United States)

2011-12-28

... or other mine to file a current report on Form 8-K with the Commission reporting receipt of certain....\\24\\ Issuers have been providing disclosure in their periodic and current reports filed with the... Release, that to the extent mine safety issues are material, under our current rules disclosure could be...

A Case Investigation of Product Structure Complexity in Mass Customization Using a Data Mining Approach

DEFF Research Database (Denmark)

Nielsen, Peter; Brunø, Thomas Ditlev; Nielsen, Kjeld

2014-01-01

This paper presents a data mining method for analyzing historical configuration data providing a number of opportunities for improving mass customization capabilities. The overall objective of this paper is to investigate how specific quantitative analyses, more specifically the association rule...

Textual Data Mining Applications in the Service Chain Knowledge Management of e-Government

Directory of Open Access Journals (Sweden)

Jalal Rezaeenour

2017-03-01

Full Text Available Systems related to knowledge management can improve quality and efficiency of knowledge used for decision making process. Approximately 80 percent of corporate information are in textual data formats. That is why text mining is useful and important in service chain knowledge management. For example, one of the most important applications of text mining is in managing on-line source of digital documents and the analysis of internal documents. This research is based on text-based documents and textual information and interviews processed by Grounded theory. In this research clustering techniques were applied at first step. In the second step, Apriori association rules techniques for discovering and extracting the most useful association rules were applied. In other words, integration of datamining techniques was emphasized to improve the accuracy and precision of classification. Using decision tree technique for classification may result in reducing classification precision. But, the proposed method showed a significant improvement in classification precision.

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

Science.gov (United States)

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.

Study on structuring the supervision system of coal mine associated with radionuclides in Xinjiang

International Nuclear Information System (INIS)

Feng Guangwen; Jia Xiahui

2012-01-01

Xinjiang is one of China's rich coal provinces (areas) and it accounts for about 40% national coal reserves. In the long-term radioactive scientific research, monitoring and environmental impact assessment works, we found parts of Yili and Hetian's coal was associated with higher radionuclide, and parts of coal seam even reached nuclear mining level. However the laws and regulations about associated radioactive coal mine supervision were not perfect, and the supervision system is still in the exploration. This article mainly started with the coal mine enterprises' geological prospecting reports, radiation environmental impact assessment and monitoring report preparation for environment acceptance checking and supervisory monitoring, controlled the coal radioactive pollution from the sources, and carried out the research of building Xinjiang associated radioactive coal mine supervision system. The establishment of supervision system will provide technical guidance for the enterprises' coal exploitation and cinders using on the one hand, and on the other hand will provide decision-making basis for strengthening the associated radioactive coal mine supervision for Xinjiang environmental regulators. (authors)

Association Rule-based Predictive Model for Machine Failure in Industrial Internet of Things

Science.gov (United States)

Kwon, Jung-Hyok; Lee, Sol-Bee; Park, Jaehoon; Kim, Eui-Jik

2017-09-01

This paper proposes an association rule-based predictive model for machine failure in industrial Internet of things (IIoT), which can accurately predict the machine failure in real manufacturing environment by investigating the relationship between the cause and type of machine failure. To develop the predictive model, we consider three major steps: 1) binarization, 2) rule creation, 3) visualization. The binarization step translates item values in a dataset into one or zero, then the rule creation step creates association rules as IF-THEN structures using the Lattice model and Apriori algorithm. Finally, the created rules are visualized in various ways for users’ understanding. An experimental implementation was conducted using R Studio version 3.3.2. The results show that the proposed predictive model realistically predicts machine failure based on association rules.

Determination of possible radiation hazards associated with tin mining industry in West Malaysia

International Nuclear Information System (INIS)

Hu, S.J.

1979-04-01

A study was made in Malaysia under an IAEA research contract on the possible radiation hazards associated with tin mining industry in Malaysia. The study comprised of the measurement of external radiation levels in various mines, gamma-ray spectrometric analysis of various samples from mines, and measurements of radon and radon daughters concentrations. For radon daughters modified Tsivoglou and Kusnetz methods were used. The study showed that there is, in general, no radiation hazard associated with the tin mining industry in West Malaysia. However, the only likely source that might pose some external radiation hazard is the amang upgrading plant which invariably concentrates either or both 232 Th and 238 U in the final products of the upgrading process. A quantitative and thorough investigation of radiation levels in the amang upgrading industry is necessary to determine the degree of hazard. No significant radon or radon daughters concentrations were noted in the underground mines

Extracting classification rules from an informatic security incidents repository by genetic programming

Directory of Open Access Journals (Sweden)

Carlos Javier Carvajal Montealegre

2015-04-01

Full Text Available This paper describes the data mining process to obtain classification rules over an information security incident data collection, explaining in detail the use of genetic programming as a mean to model the incidents behavior and representing such rules as decision trees. The described mining process includes several tasks, such as the GP (Genetic Programming approach evaluation, the individual's representation and the algorithm parameters tuning to upgrade the performance. The paper concludes with the result analysis and the description of the rules obtained, suggesting measures to avoid the occurrence of new informatics attacks. This paper is a part of the thesis work degree: Information Security Incident Analytics by Data Mining for Behavioral Modeling and Pattern Recognition (Carvajal, 2012.

VICKEY: Mining Conditional Keys on Knowledge Bases

OpenAIRE

Symeonidou , Danai; Galárraga , Luis; Pernelle , Nathalie; Saïs , Fatiha; Suchanek , Fabian

2017-01-01

International audience; A conditional key is a key constraint that is valid in only a part of the data. In this paper, we show how such keys can be mined automatically on large knowledge bases (KBs). For this, we combine techniques from key mining with techniques from rule mining. We show that our method can scale to KBs of millions of facts. We also show that the conditional keys we mine can improve the quality of entity linking by up to 47 percentage points.

Mine Water Treatment in Hongai Coal Mines

OpenAIRE

Dang Phuong Thao; Dang Vu Chi

2018-01-01

Acid mine drainage (AMD) is recognized as one of the most serious environmental problem associated with mining industry. Acid water, also known as acid mine drainage forms when iron sulfide minerals found in the rock of coal seams are exposed to oxidizing conditions in coal mining. Until 2009, mine drainage in Hongai coal mines was not treated, leading to harmful effects on humans, animals and aquatic ecosystem. This report has examined acid mine drainage problem and techniques for acid mine ...

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

Energy Technology Data Exchange (ETDEWEB)

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 of other mining operations. The environmental disturbances and procedures used in reclaiming the kaolin mined lands are reviewed and implications for planners are noted.

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

Science.gov (United States)

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…

Data Mining Methods for Recommender Systems

Science.gov (United States)

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.

Mine Water Treatment in Hongai Coal Mines

Science.gov (United States)

Dang, Phuong Thao; Dang, Vu Chi

2018-03-01

Acid mine drainage (AMD) is recognized as one of the most serious environmental problem associated with mining industry. Acid water, also known as acid mine drainage forms when iron sulfide minerals found in the rock of coal seams are exposed to oxidizing conditions in coal mining. Until 2009, mine drainage in Hongai coal mines was not treated, leading to harmful effects on humans, animals and aquatic ecosystem. This report has examined acid mine drainage problem and techniques for acid mine drainage treatment in Hongai coal mines. In addition, selection and criteria for the design of the treatment systems have been presented.

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

Science.gov (United States)

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.

[Application of association rule in mental health test for employees in a petrochemical enterprise].

Science.gov (United States)

Zhang, L F; Zhang, D N; Wang, Z P

2017-10-20

Objective: To investigate the occurrence ruleof common psychological abnormalities in petrochemical workers using association rule. Methods: From July to September,2014,the Symptom Checklist-90 (SCL-90)was used for the general survey of mental healthamong all employees in a petrochemical enterprise.The association rule Apriori algorithm was used to analyze the data of SCL-90 and investigate the occurrence rule of psychological abnormalities in petrochemical workers with different sexes,ages,or nationalities. Results: A total of 8 248 usable questionnaires were collected. The SCL-90 analysis showed that 1623 petrochemical workers(19.68%) had positive results,among whom 567(34.94%)had one positive factor and 1056 (65.06%)had two or more positive factors. A total of 7 strong association rules were identified and all of them included obsessive-compulsive symptom and depression. Male({obsessive-compulsive symptom,anxiety}=>{depression}) and female workers ({somatization,depression}=>{obsessive-compulsive symptom}) had their own special association rules. The workers aged 35-44 years had 17 special association rules,and ethnic minorities had 5 special association rules. Conclusion: Employeesin the petrochemical enterprise have multiple positive factors in SCL-90, and employees aged 35-44 years and ethnic minorities have a rich combination of psychological symptoms and need special attention during mental health intervention.

Generation of Acid Mine Lakes Associated with Abandoned Coal Mines in Northwest Turkey.

Science.gov (United States)

Sanliyuksel Yucel, Deniz; Balci, Nurgul; Baba, Alper

2016-05-01

A total of five acid mine lakes (AMLs) located in northwest Turkey were investigated using combined isotope, molecular, and geochemical techniques to identify geochemical processes controlling and promoting acid formation. All of the investigated lakes showed typical characteristics of an AML with low pH (2.59-3.79) and high electrical conductivity values (1040-6430 μS/cm), in addition to high sulfate (594-5370 mg/l) and metal (aluminum [Al], iron [Fe], manganese [Mn], nickel [Ni], and zinc [Zn]) concentrations. Geochemical and isotope results showed that the acid-generation mechanism and source of sulfate in the lakes can change and depends on the age of the lakes. In the relatively older lakes (AMLs 1 through 3), biogeochemical Fe cycles seem to be the dominant process controlling metal concentration and pH of the water unlike in the younger lakes (AMLs 4 and 5). Bacterial species determined in an older lake (AML 2) indicate that biological oxidation and reduction of Fe and S are the dominant processes in the lakes. Furthermore, O and S isotopes of sulfate indicate that sulfate in the older mine lakes may be a product of much more complex oxidation/dissolution reactions. However, the major source of sulfate in the younger mine lakes is in situ pyrite oxidation catalyzed by Fe(III) produced by way of oxidation of Fe(II). Consistent with this, insignificant fractionation between δ(34) [Formula: see text] and δ(34) [Formula: see text] values indicated that the oxidation of pyrite, along with dissolution and precipitation reactions of Fe(III) minerals, is the main reason for acid formation in the region. Overall, the results showed that acid generation during early stage formation of an AML associated with pyrite-rich mine waste is primarily controlled by the oxidation of pyrite with Fe cycles becoming the dominant processes regulating pH and metal cycles in the later stages of mine lake development.

Mine Water Treatment in Hongai Coal Mines

Directory of Open Access Journals (Sweden)

Dang Phuong Thao

2018-01-01

Full Text Available Acid mine drainage (AMD is recognized as one of the most serious environmental problem associated with mining industry. Acid water, also known as acid mine drainage forms when iron sulfide minerals found in the rock of coal seams are exposed to oxidizing conditions in coal mining. Until 2009, mine drainage in Hongai coal mines was not treated, leading to harmful effects on humans, animals and aquatic ecosystem. This report has examined acid mine drainage problem and techniques for acid mine drainage treatment in Hongai coal mines. In addition, selection and criteria for the design of the treatment systems have been presented.

Association of rule of law and health outcomes: an ecological study.

Science.gov (United States)

Pinzon-Rondon, Angela Maria; Attaran, Amir; Botero, Juan Carlos; Ruiz-Sternberg, Angela Maria

2015-10-29

To explore whether the rule of law is a foundational determinant of health that underlies other socioeconomic, political and cultural factors that have been associated with health outcomes. Global project. Data set of 96 countries, comprising 91% of the global population. The following health indicators, infant mortality rate, maternal mortality rate, life expectancy, and cardiovascular disease and diabetes mortality rate, were included to explore their association with the rule of law. We used a novel Rule of Law Index, gathered from survey sources, in a cross-sectional and ecological design. The Index is based on eight subindices: (1) Constraints on Government Powers; (2) Absence of Corruption; (3) Order and Security; (4) Fundamental Rights; (5) Open Government; (6) Regulatory Enforcement, (7) Civil Justice; and (8) Criminal Justice. The rule of law showed an independent association with infant mortality rate, maternal mortality rate, life expectancy, and cardiovascular disease and diabetes mortality rate, after adjusting for the countries' level of per capita income, their expenditures in health, their level of political and civil freedom, their Gini measure of inequality and women's status (plaw remained significant in all the multivariate models, and the following adjustment for potential confounders remained robust for at least one or more of the health outcomes across all eight subindices of the rule of law. Findings show that the higher the country's level of adherence to the rule of law, the better the health of the population. It is necessary to start considering the country's adherence to the rule of law as a foundational determinant of health. Health advocates should consider the improvement of rule of law as a tool to improve population health. Conversely, lack of progress in rule of law may constitute a structural barrier to health improvement. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a

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

Science.gov (United States)

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.

Prospectors and Developers Association of Canada Mining Matters: A Model of Effective Outreach

Science.gov (United States)

Hymers, L.; Heenan, S.

2009-05-01

Prospectors and Developers Association of Canada Mining Matters is a charitable organization whose mandate is to bring the wonders of Canada's geology and mineral resources to students, educators and industry. The organization provides current information about rocks, minerals, metals, and mining and offers exceptional educational resources, developed by teachers and for teachers that meet Junior, Intermediate and Senior Provincial Earth Science and Geography curriculum expectations. Since 1994, Mining Matters has reached more than 400,000 educators, students, industry representatives, and Aboriginal Youth through Earth Science resources. At the time of the program's inception, members of the Prospectors and Developers Association of Canada (PDAC) realized that their mining and mineral industry expertise could be of help to teachers and students. Consulting experts in education, government, and business, and the PDAC worked together to develop the first Mining Matters Earth Science curriculum kit for Grades 6 and 7 teachers in Ontario. PDAC Mining Matters became the official educational arm of the Association and a charitable organization in 1997. Since then, the organization has partnered with government, industry, and educators to develop bilingual Earth science teaching units for Grades 4 and 7, and senior High School. The teaching units consist of kits that contain curriculum correlated lesson plans, inform bulletins, genuine data sets, rock and mineral samples, equipment and additional instructional resources. Mining Matters offers instructional development workshops for the purposes of training pre-service and in- service educators to use our teaching units in the classroom. The workshops are meant to provide teachers with the knowledge and confidence they need to successfully employ the units in the classroom. Formal mechanisms for resource and workshop evaluations are in place. Overwhelmingly teacher feedback is positive, describing the excellence

Radioactivity in groundwater associated with uranium and phosphate mining and processing

International Nuclear Information System (INIS)

Kaufmann, R.F.

1981-01-01

From 1975 to 1980 USEPA investigations of the uranium and phosphate mining and milling industries addressed associated changes in the radionuclide content of nearby water resources. Available data for 226 Ra in central Florida aquifers show no significant difference in phosphate mineralized vs. nonmineralized areas. Apparently neither mineralization nor the industry cause significant increase in the Ra content of groundwater. Uranium mining and milling in a number of Western States (e.g. New Mexico, Wyoming, Colorado, Washington) cause locally increased levels of U, Ra and Th in shallow groundwater, but potable water supplies have not been adversely affected. Contamination of deep aquifers does not appear to occur, although elevated levels of Ra and U are present in many mine water discharges as a result of ore body oxidation and leaching. Model underground and surface U mines were used to evaluate chemical loading of 238 U, 226 Ra, 210 Pb and 210 Po to local and regional hydrographic units. Infiltration of mine water to potable groundwater and suspension/solution of contaminants in flood water constitute the principal elements of the aqueous pathway

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

Science.gov (United States)

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.

Using blocking approach to preserve privacy in classification rules by inserting dummy Transaction

Directory of Open Access Journals (Sweden)

Doryaneh Hossien Afshari

2017-03-01

Full Text Available The increasing rate of data sharing among organizations could maximize the risk of leaking sensitive knowledge. Trying to solve this problem leads to increase the importance of privacy preserving within the process of data sharing. In this study is focused on privacy preserving in classification rules mining as a technique of data mining. We propose a blocking algorithm to hiding sensitive classification rules. In the solution, rules' hiding occurs as a result of editing a set of transactions which satisfy sensitive classification rules. The proposed approach tries to deceive and block adversaries by inserting some dummy transactions. Finally, the solution has been evaluated and compared with other available solutions. Results show that limiting the number of attributes existing in each sensitive rule will lead to a decrease in both the number of lost rules and the production rate of ghost rules.

Data mining for the identification of metabolic syndrome status.

Science.gov (United States)

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.

Data mining for the identification of metabolic syndrome status

Science.gov (United States)

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

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

Science.gov (United States)

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.

An assessment of microbial communities associated with surface mining-disturbed overburden.

Science.gov (United States)

Poncelet, Dominique M; Cavender, Nicole; Cutright, Teresa J; Senko, John M

2014-03-01

To assess the microbiological changes that occur during the maturation of overburden that has been disturbed by surface mining of coal, a surface mining-disturbed overburden unit in southeastern Ohio, USA was characterized. Overburden from the same unit that had been disturbed for 37 and 16 years were compared to undisturbed soil from the same region. Overburden and soil samples were collected as shallow subsurface cores from each subregion of the mined area (i.e., land 16 years and 37 years post-mining, and unmined land). Chemical and mineralogical characteristics of overburden samples were determined, as were microbial respiration rates. The composition of microbial communities associated with overburden and soil were determined using culture-independent, nucleic acid-based approaches. Chemical and mineralogical evaluation of overburden suggested that weathering of disturbed overburden gave rise to a setting with lower pH and more oxidized chemical constituents. Overburden-associated microbial biomass and respiration rates increased with time after overburden disturbance. Evaluation of 16S rRNA gene libraries that were produced by "next-generation" sequencing technology revealed that recently disturbed overburden contained an abundance of phylotypes attributable to sulfur-oxidizing Limnobacter spp., but with increasing time post-disturbance, overburden-associated microbial communities developed a structure similar to that of undisturbed soil, but retained characteristics of more recently disturbed overburden. Our results indicate that over time, the biogeochemical weathering of disturbed overburden leads to the development of geochemical conditions and microbial communities that approximate those of undisturbed soil, but that this transition is incomplete after 37 years of overburden maturation.

Association between clean indoor air laws and voluntary smokefree rules in homes and cars.

Science.gov (United States)

Cheng, Kai-Wen; Okechukwu, Cassandra A; McMillen, Robert; Glantz, Stanton A

2015-03-01

This study examines the influence that smokefree workplaces, restaurants and bars have on the adoption of smokefree rules in homes and cars, and whether there is an association with adopting smokefree rules in homes and cars. Bivariate probit models were used to jointly estimate the likelihood of living in a smokefree home and having a smokefree car as a function of law coverage and other variables. Household data were obtained from the nationally representative Social Climate Survey of Tobacco Control 2001, 2002 and 2004-2009; clean indoor air law data were from the American Nonsmokers' Rights Foundation Tobacco Control Laws Database. 'Full coverage' and 'partial coverage' smokefree legislation is associated with an increased likelihood of having voluntary home and car smokefree rules compared with 'no coverage'. The association between 'full coverage' and smokefree rule in homes and cars is 5% and 4%, respectively, and the association between 'partial coverage' and smokefree rules in homes and cars is 3% and 4%, respectively. There is a positive association between the adoption of smokefree rules in homes and cars. Clean indoor air laws provide the additional benefit of encouraging voluntary adoption of smokefree rules in homes and cars. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Association of rule of law and health outcomes: an ecological study

Science.gov (United States)

Pinzon-Rondon, Angela Maria; Attaran, Amir; Botero, Juan Carlos; Ruiz-Sternberg, Angela Maria

2015-01-01

Objectives To explore whether the rule of law is a foundational determinant of health that underlies other socioeconomic, political and cultural factors that have been associated with health outcomes. Setting Global project. Participants Data set of 96 countries, comprising 91% of the global population. Primary and secondary outcome measures The following health indicators, infant mortality rate, maternal mortality rate, life expectancy, and cardiovascular disease and diabetes mortality rate, were included to explore their association with the rule of law. We used a novel Rule of Law Index, gathered from survey sources, in a cross-sectional and ecological design. The Index is based on eight subindices: (1) Constraints on Government Powers; (2) Absence of Corruption; (3) Order and Security; (4) Fundamental Rights; (5) Open Government; (6) Regulatory Enforcement, (7) Civil Justice; and (8) Criminal Justice. Results The rule of law showed an independent association with infant mortality rate, maternal mortality rate, life expectancy, and cardiovascular disease and diabetes mortality rate, after adjusting for the countries’ level of per capita income, their expenditures in health, their level of political and civil freedom, their Gini measure of inequality and women's status (pconstitute a structural barrier to health improvement. PMID:26515684

Short-term optimal operation of Three-gorge and Gezhouba cascade hydropower stations in non-flood season with operation rules from data mining

International Nuclear Information System (INIS)

Ma Chao; Lian Jijian; Wang Junna

2013-01-01

Highlights: ► Short-term optimal operation of Three-gorge and Gezhouba hydropower stations was studied. ► Key state variable and exact constraints were proposed to improve numerical model. ► Operation rules proposed were applied in population initiation step for faster optimization. ► Culture algorithm with difference evolution was selected as optimization method. ► Model and method proposed were verified by case study with feasible operation solutions. - Abstract: Information hidden in the characteristics and relationship data of a cascade hydropower stations can be extracted by data-mining approaches to be operation rules and optimization support information. In this paper, with Three-gorge and Gezhouba cascade hydropower stations as an example, two operation rules are proposed due to different operation efficiency of water turbines and tight water volume and hydraulic relationship between two hydropower stations. The rules are applied to improve optimization model with more exact decision and state variables and constraints. They are also used in the population initiation step to develop better individuals with culture algorithm with differential evolution as an optimization method. In the case study, total feasible population and the best solution based on an initial population with an operation rule can be obtained with a shorter computation time than that of a pure random initiated population. Amount of electricity generation in a dispatch period with an operation rule also increases with an average increase rate of 0.025%. For a fixed water discharge process of Three-gorge hydropower station, there is a better rule to decide an operation plan of Gezhouba hydropower station in which total hydraulic head for electricity generation is optimized and distributed with inner-plant economic operation considered.

Application of rule-based data mining techniques to real time ATLAS Grid job monitoring data

CERN Document Server

Ahrens, R; The ATLAS collaboration; Kalinin, S; Maettig, P; Sandhoff, M; dos Santos, T; Volkmer, F

2012-01-01

The Job Execution Monitor (JEM) is a job-centric grid job monitoring software developed at the University of Wuppertal and integrated into the pilot-based “PanDA” job brokerage system leveraging physics analysis and Monte Carlo event production for the ATLAS experiment on the Worldwide LHC Computing Grid (WLCG). With JEM, job progress and grid worker node health can be supervised in real time by users, site admins and shift personnel. Imminent error conditions can be detected early and countermeasures can be initiated by the Job’s owner immideatly. Grid site admins can access aggregated data of all monitored jobs to infer the site status and to detect job and Grid worker node misbehaviour. Shifters can use the same aggregated data to quickly react to site error conditions and broken production tasks. In this work, the application of novel data-centric rule based methods and data-mining techniques to the real time monitoring data is discussed. The usage of such automatic inference techniques on monitorin...

Integrated assessmet of the impacts associated with uranium mining and milling

Energy Technology Data Exchange (ETDEWEB)

Parzyck, D.C.; Baes, C.F. III; Berry, L.G.

1979-07-01

The occupational health and safety impacts are assessed for domestic underground mining, open pit mining, and milling. Public health impacts are calculated for a population of 53,000 located within 88 km (55 miles) of a typical southwestern uranium mill. The collective annual dose would be 6.5 man-lung rem/year, 89% of which is from /sup 222/Rn emitted from mill tailings. The dose to the United States population is estimated to be 6 x 10/sup 4/ man-lung rem from combined mining and milling operations. This may be comparedd with 5.7 x 10/sup 5/ man-lung rem from domestic use of natural gas and 4.4 x 10/sup 7/ man-lung rem from building interiors. Unavoidable adverse environmental impacts appear to be severe in a 250 ha area surrounding a mill site but negligible in the entire potentially impacted area (500,000 ha). The contemporary uranium resource and supply industry and its institutional settings are described in relation to the socio-economic impacts likely to emerge from high levels of uranium mining and milling. Radon and radon daughter monitoring techniques associated with uranium mining and milling are discussed.

Integrated assessmet of the impacts associated with uranium mining and milling

International Nuclear Information System (INIS)

Parzyck, D.C.; Baes, C.F. III; Berry, L.G.

1979-07-01

The occupational health and safety impacts are assessed for domestic underground mining, open pit mining, and milling. Public health impacts are calculated for a population of 53,000 located within 88 km (55 miles) of a typical southwestern uranium mill. The collective annual dose would be 6.5 man-lung rem/year, 89% of which is from 222 Rn emitted from mill tailings. The dose to the United States population is estimated to be 6 x 10 4 man-lung rem from combined mining and milling operations. This may be comparedd with 5.7 x 10 5 man-lung rem from domestic use of natural gas and 4.4 x 10 7 man-lung rem from building interiors. Unavoidable adverse environmental impacts appear to be severe in a 250 ha area surrounding a mill site but negligible in the entire potentially impacted area (500,000 ha). The contemporary uranium resource and supply industry and its institutional settings are described in relation to the socio-economic impacts likely to emerge from high levels of uranium mining and milling. Radon and radon daughter monitoring techniques associated with uranium mining and milling are discussed

Mining the bulk positron lifetime

International Nuclear Information System (INIS)

Aourag, H.; Guittom, A.

2009-01-01

We introduce a new approach to investigate the bulk positron lifetimes of new systems based on data-mining techniques. Through data mining of bulk positron lifetimes, we demonstrate the ability to predict the positron lifetimes of new semiconductors on the basis of available semiconductor data already studied. Informatics techniques have been applied to bulk positron lifetimes for different tetrahedrally bounded semiconductors in order to discover computational design rules. (copyright 2009 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

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

Science.gov (United States)

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.

TCMGeneDIT: a database for associated traditional Chinese medicine, gene and disease information using text mining

Directory of Open Access Journals (Sweden)

Chen Hsin-Hsi

2008-10-01

Full Text Available Abstract Background Traditional Chinese Medicine (TCM, a complementary and alternative medical system in Western countries, has been used to treat various diseases over thousands of years in East Asian countries. In recent years, many herbal medicines were found to exhibit a variety of effects through regulating a wide range of gene expressions or protein activities. As available TCM data continue to accumulate rapidly, an urgent need for exploring these resources systematically is imperative, so as to effectively utilize the large volume of literature. Methods TCM, gene, disease, biological pathway and protein-protein interaction information were collected from public databases. For association discovery, the TCM names, gene names, disease names, TCM ingredients and effects were used to annotate the literature corpus obtained from PubMed. The concept to mine entity associations was based on hypothesis testing and collocation analysis. The annotated corpus was processed with natural language processing tools and rule-based approaches were applied to the sentences for extracting the relations between TCM effecters and effects. Results We developed a database, TCMGeneDIT, to provide association information about TCMs, genes, diseases, TCM effects and TCM ingredients mined from vast amount of biomedical literature. Integrated protein-protein interaction and biological pathways information are also available for exploring the regulations of genes associated with TCM curative effects. In addition, the transitive relationships among genes, TCMs and diseases could be inferred through the shared intermediates. Furthermore, TCMGeneDIT is useful in understanding the possible therapeutic mechanisms of TCMs via gene regulations and deducing synergistic or antagonistic contributions of the prescription components to the overall therapeutic effects. The database is now available at http://tcm.lifescience.ntu.edu.tw/. Conclusion TCMGeneDIT is a unique database

Data mining analysis of Professor Liu Shangyi’s prescription characteristics in clinical medicine for the treatment of cancer patients with stomachache

Directory of Open Access Journals (Sweden)

Wen-Qi Huang

2018-01-01

Full Text Available Objective: To analyze National Chinese Medicine Master Liu Shangyi’s prescription characteristics of clinical medicine for the treatment of cancer patients with stomachache. Methods: Data on prescriptions for cancer patients with stomachache between January 2014 and July 2016 were collected. The composing principles were analyzed by unsupervised data mining methods including Apriori algorithm in association rules and complex system entropy cluster. Results: Based on the analysis of 120 prescriptions, the frequency of each herb and association rules among the herbs were computed. Four core combinations and two new prescriptions were mined from the database. Compared to the before treatment, the clinical symptomatic grading of stomachache after treatment was lower (P < 0.001. Conclusion: Professor Liu has been successful in the treatment of cancer patients with stomachache by prescribing medication that aids in activating blood circulation, removing dampness, and alleviating pain.

An inductive database prototype based on virtual mining views

NARCIS (Netherlands)

Blockeel, H.; Calders, T.; Fromont, É.; Goethals, B.; Prado, A.; Robardet, C.

2008-01-01

We present a prototype of an inductive database. Our system enables the user to query not only the data stored in the database but also generalizations (e.g. rules or trees) over these data through the use of virtual mining views. The mining views are relational tables that virtually contain the

A Multi-Agent Framework for Anomalies Detection on Distributed Firewalls Using Data Mining Techniques

Science.gov (United States)

Karoui, Kamel; Ftima, Fakher Ben; Ghezala, Henda Ben

The Agents and Data Mining integration has emerged as a promising area for disributed problems solving. Applying this integration on distributed firewalls will facilitate the anomalies detection process. In this chapter, we present a set of algorithms and mining techniques to analyse, manage and detect anomalies on distributed firewalls' policy rules using the multi-agent approach; first, for each firewall, a static agent will execute a set of data mining techniques to generate a new set of efficient firewall policy rules. Then, a mobile agent will exploit these sets of optimized rules to detect eventual anomalies on a specific firewall (intra-firewalls anomalies) or between firewalls (inter-firewalls anomalies). An experimental case study will be presented to demonstrate the usefulness of our approach.

DATA MINING IN EDUCATION: CURRENT STATE AND PERSPECTIVES OF DEVELOPMENT

Directory of Open Access Journals (Sweden)

Yurii O. Kovalchuk

2016-01-01

Full Text Available The main tasks (classification and regression, association rules, clustering and the basic principles of the Data Mining algorithms in the context of their use for a variety of research in the field of education which are the subject of a relatively new independent direction Educational Data Mining are considered. The findings about the most popular topics of research within this area as well as the perspectives of its development are presented. Presentation of the material is illustrated by simple examples. This article is intended for readers who are engaged in research in the field of education at various levels, especially those involved in the use of e-learning systems, but little familiar with this area of data analysis.

An application of data mining techniques in designing catalogue for a laundry service

Directory of Open Access Journals (Sweden)

Khasanah Annisa Uswatun

2018-01-01

Full Text Available Catalogues are the media that companies use to promote their products or services. Since catalogue is one of marketing media, the first essential step before designing product catalogue is determining the market target. Besides, it is also important to put some information that appeal to the target market, such as discount or promos by analysing customer pattern preferences in using services or buying product. This study conduct two data mining technique. The first is clustering analysis to segment customer and the second one is association rule mining to discover an interesting pattern about the services that commonly used by the customer at the same service time. Thus, the results will be used as a recommendation to make an attractive marketing strategy to be put in the service catalogue promo for a laundry in Sleman Yogyakarta. The clustering result showed that the biggest customer segment is university student who come 3 until 5 times in a month on weekends, while the association rule result showed that clothes, shoes, and bed sheet have strong relationship. The catalogue design is presented in the end of the paper.

Association rule mining data for census tract chemical exposure analysis

Data.gov (United States)

U.S. Environmental Protection Agency — Chemical concentration, exposure, and health risk data for U.S. census tracts from National Scale Air Toxics Assessment (NATA). This dataset is associated with the...

Mining association patterns of drug-interactions using post marketing FDA's spontaneous reporting data.

Science.gov (United States)

Ibrahim, Heba; Saad, Amr; Abdo, Amany; Sharaf Eldin, A

2016-04-01

Pharmacovigilance (PhV) is an important clinical activity with strong implications for population health and clinical research. The main goal of PhV is the timely detection of adverse drug events (ADEs) that are novel in their clinical nature, severity and/or frequency. Drug interactions (DI) pose an important problem in the development of new drugs and post marketing PhV that contribute to 6-30% of all unexpected ADEs. Therefore, the early detection of DI is vital. Spontaneous reporting systems (SRS) have served as the core data collection system for post marketing PhV since the 1960s. The main objective of our study was to particularly identify signals of DI from SRS. In addition, we are presenting an optimized tailored mining algorithm called "hybrid Apriori". The proposed algorithm is based on an optimized and modified association rule mining (ARM) approach. A hybrid Apriori algorithm has been applied to the SRS of the United States Food and Drug Administration's (U.S. FDA) adverse events reporting system (FAERS) in order to extract significant association patterns of drug interaction-adverse event (DIAE). We have assessed the resulting DIAEs qualitatively and quantitatively using two different triage features: a three-element taxonomy and three performance metrics. These features were applied on two random samples of 100 interacting and 100 non-interacting DIAE patterns. Additionally, we have employed logistic regression (LR) statistic method to quantify the magnitude and direction of interactions in order to test for confounding by co-medication in unknown interacting DIAE patterns. Hybrid Apriori extracted 2933 interacting DIAE patterns (including 1256 serious ones) and 530 non-interacting DIAE patterns. Referring to the current knowledge using four different reliable resources of DI, the results showed that the proposed method can extract signals of serious interacting DIAEs. Various association patterns could be identified based on the relationships among

Explaining and predicting workplace accidents using data-mining techniques

International Nuclear Information System (INIS)

Rivas, T.; Paz, M.; Martin, J.E.; Matias, J.M.; Garcia, J.F.; Taboada, J.

2011-01-01

Current research into workplace risk is mainly conducted using conventional descriptive statistics, which, however, fail to properly identify cause-effect relationships and are unable to construct models that could predict accidents. The authors of the present study modelled incidents and accidents in two companies in the mining and construction sectors in order to identify the most important causes of accidents and develop predictive models. Data-mining techniques (decision rules, Bayesian networks, support vector machines and classification trees) were used to model accident and incident data compiled from the mining and construction sectors and obtained in interviews conducted soon after an incident/accident occurred. The results were compared with those for a classical statistical techniques (logistic regression), revealing the superiority of decision rules, classification trees and Bayesian networks in predicting and identifying the factors underlying accidents/incidents.

Explaining and predicting workplace accidents using data-mining techniques

Energy Technology Data Exchange (ETDEWEB)

Rivas, T., E-mail: trivas@uvigo.e [Dpto. Ingenieria de los Recursos Naturales y Medio Ambiente, E.T.S.I. Minas, University of Vigo, Campus Lagoas, 36310 Vigo (Spain); Paz, M., E-mail: mpaz.minas@gmail.co [Dpto. Ingenieria de los Recursos Naturales y Medio Ambiente, E.T.S.I. Minas, University of Vigo, Campus Lagoas, 36310 Vigo (Spain); Martin, J.E., E-mail: jmartin@cippinternacional.co [CIPP International, S.L. Parque Tecnologico de Asturias, Parcela 43, Oficina 11, 33428 Llanera (Spain); Matias, J.M., E-mail: jmmatias@uvigo.e [Dpto. Estadistica e Investigacion Operativa, E.T.S.I. Minas, University of Vigo, Campus Lagoas, 36310 Vigo (Spain); Garcia, J.F., E-mail: jgarcia@cippinternacional.co [CIPP International, S.L. Parque Tecnologico de Asturias, Parcela 43, Oficina 11, 33428 Llanera (Spain); Taboada, J., E-mail: jtaboada@uvigo.e [Dpto. Ingenieria de los Recursos Naturales y Medio Ambiente, E.T.S.I. Minas, University of Vigo, Campus Lagoas, 36310 Vigo (Spain)

2011-07-15

Current research into workplace risk is mainly conducted using conventional descriptive statistics, which, however, fail to properly identify cause-effect relationships and are unable to construct models that could predict accidents. The authors of the present study modelled incidents and accidents in two companies in the mining and construction sectors in order to identify the most important causes of accidents and develop predictive models. Data-mining techniques (decision rules, Bayesian networks, support vector machines and classification trees) were used to model accident and incident data compiled from the mining and construction sectors and obtained in interviews conducted soon after an incident/accident occurred. The results were compared with those for a classical statistical techniques (logistic regression), revealing the superiority of decision rules, classification trees and Bayesian networks in predicting and identifying the factors underlying accidents/incidents.

A Data Mining and Survey Study on Diseases Associated with Paraesophageal Hernia

OpenAIRE

Yang, Jianji; Logan, Judith

2006-01-01

Paraesophageal hernia is a severe form of hiatal hernia, characterized by the upward dislocation of the gastric fundus into the thoracic cavity. In this study, the 1999 National Inpatient Sample dataset of the Healthcare Cost and Utilization Project was analyzed using data mining techniques to explore disorders associated with paraesophageal hernia. The result of this data mining process was compared with a subsequent expert knowledge survey of 97 gastrointestinal tract surgeons. This two-ste...

Critical analysis of the Colombian mining legislation

International Nuclear Information System (INIS)

Vargas P, Elkin; Gonzalez S, Carmen Lucia

2003-01-01

The document analyses the Colombian mining legislation, Act 685 of 2001, based on the reasons expressed by the government and the miners for its conceit and approval. The document tries to determine the developments achieved by this new Mining Code considering international mining competitiveness and its adaptation to the constitutional rules about environment, indigenous communities, decentralization and sustainable development. The analysis formulates general and specific hypothesis about the proposed objectives of the reform, which are confronted with the arguments and critical evaluations of the results. Most hypothesis are not verified, thus demonstrating that the Colombian mining legislation is far from being the necessary instrument to promote mining activities, making it competitive according to international standards and adapted to the principles of sustainable development, healthy environment, community participation, ethnic minorities and regional autonomy

Discovering Sentinel Rules for Business Intelligence

Science.gov (United States)

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.

Assessment of pneumoconiosis hazards associated with mining operations in coal mines. [USSR

Energy Technology Data Exchange (ETDEWEB)

Sukhanov, V V; Menyailo, N I; Petul' ko, S N

1984-07-01

Methods are discussed for evaluating hazards of pneumoconiosis in underground coal mines. Pneumoconiosis hazards are decisively influenced by: content of respirable dusts in mine air at a working place, dust composition, temperature and time of a miner's contact with dusts. The following classification of pneumoconiosis hazards is used in the USSR: low hazards when a miner is endangered by pneumoconiosis after 20 years or more, medium hazards when pneumoconiosis may occur after 10 to 20 years, high pneumoconiosis hazards when a miner is endangered by pneumoconiosis after less than 10 years of contact with dusts. High air temperature in deep coal mines increases pneumoconiosis hazards: when temperature exceeds 26 C a temperature increase of 1 C causes a 10% increase in dust chemical activity. Safety standards which describe the maximum permissible dust level in coal mine air in the USSR, the FRG, France and Poland are compared.

Association and Sequence Mining in Web Usage

Directory of Open Access Journals (Sweden)

Claudia Elena DINUCA

2011-06-01

Full Text Available Web servers worldwide generate a vast amount of information on web users’ browsing activities. Several researchers have studied these so-called clickstream or web access log data to better understand and characterize web users. Clickstream data can be enriched with information about the content of visited pages and the origin (e.g., geographic, organizational of the requests. The goal of this project is to analyse user behaviour by mining enriched web access log data. With the continued growth and proliferation of e-commerce, Web services, and Web-based information systems, the volumes of click stream and user data collected by Web-based organizations in their daily operations has reached astronomical proportions. This information can be exploited in various ways, such as enhancing the effectiveness of websites or developing directed web marketing campaigns. The discovered patterns are usually represented as collections of pages, objects, or re-sources that are frequently accessed by groups of users with common needs or interests. The focus of this paper is to provide an overview how to use frequent pattern techniques for discovering different types of patterns in a Web log database. In this paper we will focus on finding association as a data mining technique to extract potentially useful knowledge from web usage data. I implemented in Java, using NetBeans IDE, a program for identification of pages’ association from sessions. For exemplification, we used the log files from a commercial web site.

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

Science.gov (United States)

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.

Arsenic and antimony geochemistry of mine wastes, associated waters and sediments at the Giant Mine, Yellowknife, Northwest Territories, Canada

International Nuclear Information System (INIS)

Fawcett, Skya E.; Jamieson, Heather E.; Nordstrom, D. Kirk; McCleskey, R. Blaine

2015-01-01

Sb were associated with organic material and appeared mobile in the root zone. In the zone below active plant growth, As and Sb were associated primarily with inorganic phases suggesting a release and reprecipitation of these elements upon plant death. The co-existence of reduced and oxidized As and Sb species, instability of some phases under changing redox conditions, and plant uptake and release pose challenges for remediation efforts at the mine.

Ecological and human health risks associated with abandoned gold mine tailings contaminated soil

DEFF Research Database (Denmark)

Ngole-Jeme, Veronica Mpode; Fantke, Peter

2017-01-01

of arsenic (As), cadmium (Cd), chromium (Cr), cobalt (Co), copper (Cu), lead (Pb), manganese (Mn), nickel (Ni), and zinc (Zn) in soil samples from the area varied with the highest contamination factors (expressed as ratio of metal or metalloid concentration in the tailings contaminated soil......Gold mining is a major source of metal and metalloid emissions into the environment. Studies were carried out in Krugersdorp, South Africa, to evaluate the ecological and human health risks associated with exposure to metals and metalloids in mine tailings contaminated soils. Concentrations......×10−2 for As and Ni respectively among children, and 5×10−3 and 4×10−3 for As and Ni respectively among adults. There is significant potential ecological and human health risk associated with metal and metalloid exposure from contaminated soils around gold mine tailings dumps. This could be a potential contributing...

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

Science.gov (United States)

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.

Legal instruments for controlling exposure of workers to ionizing radiations in mining and its associated industries

International Nuclear Information System (INIS)

Yusoff Ismail

1994-01-01

Briefly, the existing legal instruments for protection of workers in mining and its associated activities are given. Further, major provisions of the laws relevant to the protection of workers against ionizing radiations in mining and its associated activities are detailed. Finally, practical framework developed by the Atomic Energy Licensing Board, for implementation and enforcement is described

Parental rules and communication: their association with adolescent smoking

NARCIS (Netherlands)

Harakeh, Z.; Scholte, R.H.J.; Vries, H. de; Engels, R.C.M.E.

2005-01-01

Aims - To examine the association between parental rules and communication (also referred to as antismoking socialization) and adolescents’ smoking. Design and participants - A cross-sectional study including 428 Dutch two-parent families with at least two adolescent children (aged

A Swarm Optimization approach for clinical knowledge mining.

Science.gov (United States)

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 �

Arsenic and antimony geochemistry of mine wastes, associated waters and sediments at the Giant Mine, Yellowknife, Northwest Territories, Canada

Science.gov (United States)

Fawcett, Skya E.; Jamieson, Heather E.; Nordstrom, D. Kirk; McCleskey, R. Blaine

2015-01-01

Elevated levels of arsenic (As) and antimony (Sb) in water and sediments are legacy residues found downstream from gold-mining activities at the Giant Mine in Yellowknife, Northwest Territories (NWT), Canada. To track the transport and fate of As and Sb, samples of mine-waste from the mill, and surface water, sediment, pore-water, and vegetation downstream of the mine were collected. Mine waste, pore-water, and sediment samples were analyzed for bulk chemistry, and aqueous and solid-state speciation. Sediment and vegetation chemistry were evaluated using scanning electron microscope imaging, synchrotron-based element mapping and electron microprobe analysis. The distributions of As and Sb in sediments were similar, yet their distributions in the corresponding pore-waters were mostly dissimilar, and the mobility of As was greater than that of Sb. Competition for sorption sites is the most likely cause of elevated Sb concentrations in relatively oxidized pore-water and surface water. The aqueous and solid-state speciation of As and Sb also differed. In pore-water, As(V) dominated in oxidizing environments and As(III) in reducing environments. In contrast, the Sb(V) species dominated in all but one pore-water sample, even under reducing conditions. Antimony(III) appears to preferentially precipitate or adsorb onto sulfides as evidenced by the prevalence of an Sb(III)-S secondary solid-phase and the lack of Sb(III)(aq) in the deeper zones. The As(V)–O solid phase became depleted with depth below the sediment–water interface, and the Sb(V)–O phase persisted under relatively reducing conditions. In the surficial zone at a site populated by Equisetum fluviatile (common horsetail), As and Sb were associated with organic material and appeared mobile in the root zone. In the zone below active plant growth, As and Sb were associated primarily with inorganic phases suggesting a release and reprecipitation of these elements upon plant death. The co-existence of reduced

Parental rules and communication: their association with adolescent smoking.

Science.gov (United States)

Harakeh, Zeena; Scholte, Ron H J; de Vries, Hein; Engels, Rutger C M E

2005-06-01

To examine the association between parental rules and communication (also referred to as antismoking socialization) and adolescents' smoking. A cross-sectional study including 428 Dutch two-parent families with at least two adolescent children (aged 13-17 years). Parents' and adolescents' reports on an agreement regarding smoking by adolescents, smoking house rules, parental confidence in preventing their child from smoking, frequency and quality of communication about smoking, and parent's reactions to smoking experimentation. Compared with fathers and adolescents, mothers reported being more involved in antismoking socialization. There were robust differences in antismoking socialization efforts between smoking and non-smoking parents. Perceived parental influence and frequency and quality of communication about smoking were associated with adolescents' smoking. The association between antismoking socialization practices and adolescents' smoking was not moderated by birth order, parents' smoking or gender of the adolescent. Encouraging parents, whether or not they themselves smoke, to discuss smoking-related issues with their children in a constructive and respectful manner is worth exploring as an intervention strategy to prevent young people taking up smoking.

A study of trends in occupational risks associated with coal mining

Energy Technology Data Exchange (ETDEWEB)

Amoundru, C.

1980-10-01

The occupational risks associated with underground coal mining can be categorized as either industrial accidents or occupational diseases. Since 1957, the number of fatal accidents per million tons of coal produced has dropped by a factor of four. The number of industrial accidents in general decreased by 30% during 1967-75. The main occupational diseases affecting miners are arthrosis, deafness, and pneumoconiosis. To make an objective comparison with the health hazards from other sources of energy, the probable risks facing workers in a modern mine should be compared with those currently confronting workers in other industries.

Sustainable Mining Environment: Technical Review of Post-mining Plans

Directory of Open Access Journals (Sweden)

Restu Juniah

2017-12-01

Full Text Available The mining industry exists because humans need mining commodities to meet their daily needs such as motor vehicles, mobile phones, electronic equipment and others. Mining commodities as mentioned in Government Regulation No. 23 of 2010 on Implementation of Mineral and Coal Mining Business Activities are radioactive minerals, metal minerals, nonmetallic minerals, rocks and coal. Mineral and coal mining is conducted to obtain the mining commodities through production operations. Mining and coal mining companies have an obligation to ensure that the mining environment in particular after the post production operation or post mining continues. The survey research aims to examine technically the post-mining plan in coal mining of PT Samantaka Batubara in Indragiri Hulu Regency of Riau Province towards the sustainability of the mining environment. The results indicate that the post-mining plan of PT Samantaka Batubara has met the technical aspects required in post mining planning for a sustainable mining environment. Postponement of post-mining land of PT Samantaka Batubara for garden and forest zone. The results of this study are expected to be useful and can be used by stakeholders, academics, researchers, practitioners and associations of mining, and the environment.

Respirable quartz hazard associated with coal mine roof bolter dust

International Nuclear Information System (INIS)

Joy, G.J.; Beck, T.W.; Listak, J.M.

2010-01-01

Pneumoconiosis has been reported to be increasing among underground coal miners in the Southern Appalachian Region. The National Institute for Occupational Safety and Health conducted a study to examine the particle size distribution and quartz content of dust generated by the installation of roof bolts in mines. Forty-six bulk samples of roof bolting machine pre-cleaner cyclone dump dust and collector box dust were collected from 26 underground coal mines. Real-time and integrated airborne respirable dust concentrations were measured on 3 mining sections in 2 mines. The real-time airborne dust concentrations profiles were examined to identify any concentration changes that might be associated with pre-cleaner cyclone dust discharge events. The study showed that bolter dust is a potential inhalation hazard due to the fraction of dust less than 10 μm in size, and the quartz content of the dust. The pre-cleaner cyclone dust was significantly larger than the collector box dust, indicating that the pre-cleaner functioned properly in removing the larger dust size fraction from the airstream. However, the pre-cleaner dust still contained a substantial amount of respirable dust. It was concluded that in order to maintain the effectiveness of a roof bolter dust collector, periodic removal of dust is required. Appropriate work procedures and equipment are necessary to minimize exposure during this cleaning task. 13 refs., 3 tabs., 2 figs.

Bioremediation of mine water.

Science.gov (United States)

Klein, Robert; Tischler, Judith S; Mühling, Martin; Schlömann, Michael

2014-01-01

Caused by the oxidative dissolution of sulfide minerals, mine waters are often acidic and contaminated with high concentrations of sulfates, metals, and metalloids. Because the so-called acid mine drainage (AMD) affects the environment or poses severe problems for later use, treatment of these waters is required. Therefore, various remediation strategies have been developed to remove soluble metals and sulfates through immobilization using physical, chemical, and biological approaches. Conventionally, iron and sulfate-the main pollutants in mine waters-are removed by addition of neutralization reagents and subsequent chemical iron oxidation and sulfate mineral precipitation. Biological treatment strategies take advantage of the ability of microorganisms that occur in mine waters to metabolize iron and sulfate. As a rule, these can be grouped into oxidative and reductive processes, reflecting the redox state of mobilized iron (reduced form) and sulfur (oxidized form) in AMD. Changing the redox states of iron and sulfur results in iron and sulfur compounds with low solubility, thus leading to their precipitation and removal. Various techniques have been developed to enhance the efficacy of these microbial processes, as outlined in this review.

Environmental impacts associated with an abandoned mine in the Witbank Coalfield, South Africa

International Nuclear Information System (INIS)

Bell, F.G.; Bullock, S.E.T.; Haelbich, T.F.J.; Lindsay, P.

2001-01-01

Mining at Middelburg Colliery in the Witbank Coalfield commenced at the turn of the last century. Initially, there was little environmental degradation associated with mining activities; however, in the late 1930s, a pillar-robbing programme commenced. This has had a marked effect on the environment. Some of the most notable primary effects include subsidence, the appearance of tension cracks at the surface and crownhole development. Secondary effects include spontaneous combustion of the coal worked, as air has been provided with ready access to the mine, accelerated subsidence due to the strength of many pillars being reduced by burning, and a marked deterioration of groundwater quality in the area due to the seepage of acid mine drainage from the mine. Spoil heaps also form blemishes on the landscape. These contain significant amounts of coal and have undergone spontaneous combustion. The deterioration in the quality of water has led to the decimation of vegetation in some areas and the eradication of aquatic flora and fauna in a nearby stream

Arsenic associated with historical gold mining in the Sierra Nevada foothills: Case study and field trip guide for Empire Mine State Historic Park, California

Science.gov (United States)

Alpers, Charles N.; Myers, Perry A; Millsap, Daniel; Regnier, Tamsen B; Bowell, Robert J.; Alpers, Charles N.; Jamieson, Heather E.; Nordstrom, D. Kirk; Majzlan, Juraj

2014-01-01

The Empire Mine, together with other mines in the Grass Valley mining district, produced at least 21.3 million troy ounces (663 tonnes) of gold (Au) during the 1850s through the 1950s, making it the most productive hardrock Au mining district in California history (Clark 1970). The Empire Mine State Historic Park (Empire Mine SHP or EMSHP), established in 1975, provides the public with an opportunity to see many well-preserved features of the historic mining and mineral processing operations (CDPR 2014a).A legacy of Au mining at Empire Mine and elsewhere is contamination of mine wastes and associated soils, surface waters, and groundwaters with arsenic (As), mercury (Hg), lead (Pb), and other metals. At EMSHP, As has been the principal contaminant of concern and the focus of extensive remediation efforts over the past several years by the State of California, Department of Parks and Recreation (DPR) and Newmont USA, Ltd. In addition, the site is the main focus of a multidisciplinary research project on As bioavailability and bioaccessibility led by the California Department of Toxic Substances Control (DTSC) and funded by the U.S. Environmental Protection Agency’s (USEPA’s) Brownfields Program.This chapter was prepared as a guide for a field trip to EMSHP held on June 14, 2014, in conjunction with a short course on “Environmental Geochemistry, Mineralogy, and Microbiology of Arsenic” held in Nevada City, California on June 15–16, 2014. This guide contains background information on geological setting, mining history, and environmental history at EMSHP and other historical Au mining districts in the Sierra Nevada, followed by descriptions of the field trip stops.

Planning, implementation and analysis of mine-surveying measurements to detect rock movements at the Asse salt mine

International Nuclear Information System (INIS)

Hensel, G.

1991-01-01

At the Asse pit, a former salt mine, research has been done since 1965 mainly for the ultimate disposal of radioactive wastes. Within this framework a mine-surveying measurement program has been developed to detect local and extensive rock movements in the mine structure and on the surface. The rock observation program consists of surface levelling, levellings in the mine structure, measurement of shaft depth, shaft sounding, position and gyroscopic measurements as well as cavity convergence and extensometer measurements. The results of that measuring program are taken into account to judge stability. The subject of this work is to analyse the position measurements by priorities to find out to which extent the results, that is the horizontal displacement components, are interpretable. Such analysis is carried out according to the rules of compensating calculation by means of strict compensation after mediating observations. (HS) [de

Mathematical tools for data mining set theory, partial orders, combinatorics

CERN Document Server

Simovici, Dan A

2014-01-01

Data mining essentially relies on several mathematical disciplines, many of which are presented in this second edition of this book. Topics include partially ordered sets, combinatorics, general topology, metric spaces, linear spaces, graph theory. To motivate the reader a significant number of applications of these mathematical tools are included ranging from association rules, clustering algorithms, classification, data constraints, logical data analysis, etc. The book is intended as a reference for researchers and graduate students. The current edition is a significant expansion of the firs

Associations between rule-based parenting practices and child screen viewing: A cross-sectional study

Directory of Open Access Journals (Sweden)

Joanna M. Kesten

2015-01-01

Conclusions: Limit setting is associated with greater SV. Collaborative rule setting may be effective for managing boys' game-console use. More research is needed to understand rule-based parenting practices.

An improved Pearson's correlation proximity-based hierarchical clustering for mining biological association between genes.

Science.gov (United States)

Booma, P M; Prabhakaran, S; Dhanalakshmi, R

2014-01-01

Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists. Data mining that extracts the hidden and usual information from datasets fails to identify the most significant biological associations between genes. A search made with heuristic for standard biological process measures only the gene expression level, threshold, and response time. Heuristic search identifies and mines the best biological solution, but the association process was not efficiently addressed. To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure of improved Pearson's correlation (PCPHC). Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association between the clusters. Moreover, a GL-PCPHC applies pattern growing method to mine the PCPHC patterns. Compared to existing gene expression analysis, the PCPHC model achieves better performance. Experimental evaluations are conducted for GL-PCPHC model with standard benchmark gene expression datasets extracted from UCI repository and GenBank database in terms of execution time, size of pattern, significance level, biological association efficiency, and pattern quality.

Acid mine drainage: mining and water pollution issues in British Columbia

Energy Technology Data Exchange (ETDEWEB)

NONE

1998-12-31

The importance of protecting water quality and some of the problems associated with mineral development are described. Negative impacts of mining operations such as sedimentation, water disturbances, and water pollution from waste rock and tailings are considered. Mining wastes, types of water pollution from mining, the legacy of acid mine drainage, predicting acid mine drainage, preventing and mitigating acid mine drainage, examples from the past, and cyanide heap-leaching are discussed. The real costs of mining at the Telkwa open pit coal mine are assessed. British Columbia mines that are known for or are potentially acid generating are shown on a map. 32 refs., 10 figs.

FLEXOWELL vertical lift systems in underground mining and construction industries

Energy Technology Data Exchange (ETDEWEB)

Paelke, J.W.

1988-06-01

Mining and quarrying companies are seeking improved and more continuous transport methods to reduce their costs. Frequently in the past the use of conveyors has been ruled out in steep mining applications but now the Scholtz FLEXOWELL belting which can be used at angles up to the vertical will enable many mines to consider complete belt conveyor systems for the first time. Applications will include steep conveyors for surface mines and quarries in order to eliminate the need for expensive and noisy fleets of trucks and the associated requirements to maintain haul roads. A further field is in the use of steep or vertical conveyors in underground mines to ensure improved continuity of output in existing shaft systems or reduced development costs in new mines. The Scholtz company, a member of the Nokia Group which had sales of about 3.5 billion U.S. Dollars in 1987, has more than 20 years experience with their FLEXOWELL belts. Over 40,000 units are operating around the world. These are already fully proven for vertical lifts of over 100 m (328ft) and up to 500 m (1,640 ft) is possible. Tonnage ratings of up to 4,000 t/h are achievable. Widespread acceptance of this technology has resulted in unique and major installations over the past few years. This paper reviews various applications - from the viewpoint of successfully proven vertical lift systems as well as the maintenance and downtime aspects. 3 refs., 9 figs.

Mining top-k frequent closed itemsets in data streams using sliding window

International Nuclear Information System (INIS)

Rehman, Z.; Shahbaz, M.

2013-01-01

Frequent itemset mining has become a popular research area in data mining community since the last few years. T here are two main technical hitches while finding frequent itemsets. First, to provide an appropriate minimum support value to start and user need to tune this minimum support value by running the algorithm again and again. Secondly, generated frequent itemsets are mostly numerous and as a result a number of association rules generated are also very large in numbers. Applications dealing with streaming environment need to process the data received at high rate, therefore, finding frequent itemsets in data streams becomes complex. In this paper, we present an algorithm to mine top-k frequent closed itemsets using sliding window approach from streaming data. We developed a single-pass algorithm to find frequent closed itemsets of length between user's defined minimum and maximum- length. To improve the performance of algorithm and to avoid rescanning of data, we have transformed data into bitmap based tree data structure. (author)

Implementasi Data Warehouse dan Data Mining: Studi Kasus Analisis Peminatan Studi Siswa

Directory of Open Access Journals (Sweden)

Eka Miranda

2011-06-01

Full Text Available This paper discusses the implementation of data mining and their role in helping decision-making related to students’ specialization program selection. Currently, the university uses a database to store records of transactions which can not directly be used to assist analysis and decision making. Based on these issues then made the data warehouse design used to store large amounts of data and also has the potential to gain new data distribution perspectives and allows to answer the ad hoc question as well as to perform data analysis. The method used consists of: record analysis related to students’ academic achievement, designing data warehouse and data mining. The paper’s results are in a form of data warehouse and data mining design and its implementation with the classification techniques and association rules. From these results can be seen the students’ tendency and pattern background in choosing the specialization, to help them make decisions. 

77 FR 20700 - Examinations of Work Areas in Underground Coal Mines for Violations of Mandatory Health or Safety...

Science.gov (United States)

2012-04-06

... DEPARTMENT OF LABOR Mine Safety and Health Administration 30 CFR Part 75 RIN 1219-AB75 Examinations of Work Areas in Underground Coal Mines for Violations of Mandatory Health or Safety Standards AGENCY: Mine Safety and Health Administration, Labor. ACTION: Final rule. SUMMARY: The Mine Safety and...

Preference Mining Using Neighborhood Rough Set Model on Two Universes.

Science.gov (United States)

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.

Prediction of users webpage access behaviour using association ...

Indian Academy of Sciences (India)

pages mainly depended on the support and lift measure whereas confidence assumed ... Apriori algorithm; association rules; data mining; MSNBC; web usage .... clustering was used in finding the user access patterns from web access log. .... satisfied the minimum support and confidence of 0.6% and 100% respectively.

Integrated Association Rules Complete Hiding Algorithms

Directory of Open Access Journals (Sweden)

Mohamed Refaat Abdellah

2017-01-01

Full Text Available This paper presents database security approach for complete hiding of sensitive association rules by using six novel algorithms. These algorithms utilize three new weights to reduce the needed database modifications and support complete hiding, as well as they reduce the knowledge distortion and the data distortions. Complete weighted hiding algorithms enhance the hiding failure by 100%; these algorithms have the advantage of performing only a single scan for the database to gather the required information to form the hiding process. These proposed algorithms are built within the database structure which enables the sanitized database to be generated on run time as needed.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Exposure to dust and particle-associated 1-nitropyrene of drivers of diesel-powered equipment in underground mining.

Science.gov (United States)

Scheepers, P T J; Micka, V; Muzyka, V; Anzion, R; Dahmann, D; Poole, J; Bos, R P

2003-07-01

A field study was conducted in two mines in order to determine the most suitable strategy for ambient exposure assessment in the framework of a European study aimed at validation of biological monitoring approaches for diesel exhaust (BIOMODEM). Exposure to dust and particle-associated 1-nitropyrene (1-NP) was studied in 20 miners of black coal by the long wall method (Czech Republic) and in 20 workers in oil shale mining by the room and pillar method (Estonia). The study in the oil shale mine was extended to include 100 workers in a second phase (main study). In each mine half of the study population worked underground as drivers of diesel-powered trains (black coal) and excavators (oil shale). The other half consisted of workers occupied in various non-diesel production assignments. Exposure to diesel exhaust was studied by measurement of inhalable and respirable dust at fixed locations and by personal air sampling of respirable dust. The ratio of geometric mean inhalable to respirable dust concentration was approximately two to one. The underground/surface ratio of respirable dust concentrations measured at fixed locations and in the breathing zones of the workers was 2-fold or greater. Respirable dust was 2- to 3-fold higher in the breathing zone than at fixed sampling locations. The 1-NP content in these dust fractions was determined by gas chromatography-mass spectrometry/mass spectrometry and ranged from 0.003 to 42.2 ng/m(3) in the breathing zones of the workers. In mine dust no 1-NP was detected. In both mines 1-NP was observed to be primarily associated with respirable particles. The 1-NP concentrations were also higher underground than on the surface (2- to 3-fold in the coal mine and 10-fold or more in the oil shale mine). Concentrations of 1-NP in the breathing zones were also higher than at fixed sites (2.5-fold in the coal mine and 10-fold in the oil shale mine). For individual exposure assessment personal air sampling is preferred over air sampling

Radiation protection programme for uranium mining

International Nuclear Information System (INIS)

Mbeye, M.J.

2014-04-01

The Radiation Protection Programme (RPP) was developed to ensure that measures are in place for the occupational protection and safety in uranium mining facility. This work has established a number of protective measures that should be taken by the individual miners, licensee and all staff. It is not known whether Kayerekera Uranium mine has the technical and administrative capability for an effective radiation protection programme. The key in the mining facility is the control of dust through various means to prevent the escape of radon gas. Personal hygiene and local operating rules have been discovered to be very important for the protection and safety of the workers. The following components have also been discovered to be vital in ensuring safety culture in the mining facility: classification of working areas, monitoring of individuals and workplace, assignment of responsibilities, emergency preparedness, education and training and health surveillance. The regulatory body (Environmental Affairs Department of Malawi) should examine the major areas outlined in the RPP for Kayerekera uranium mine to find out the effectiveness of the RPP that is in place. (au)

Biclustering Learning of Trading Rules.

Science.gov (United States)

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.

Profiling high-frequency accident locations using association rules

OpenAIRE

GEURTS, Karolien; WETS, Geert; BRIJS, Tom; VANHOOF, Koen

2003-01-01

In Belgium, traffic safety is currently one of the government's highest priorities. Identifying and profiling black spots and black zones in terms of accident related data and location characteristics must provide new insights into the complexity and causes of road accidents, which, in ram, provide valuable input for government actions. In this paper, association rules are used to identify accident circumstances that frequently occur together at high frequency accident locations. Furthermore...

CSHURI - Modified HURI algorithm for Customer Segmentation and Transaction Profitability

OpenAIRE

Pillai, Jyothi; Vyas, O. P.

2012-01-01

Association rule mining (ARM) is the process of generating rules based on the correlation between the set of items that the customers purchase.Of late, data mining researchers have improved upon the quality of association rule mining for business development by incorporating factors like value (utility), quantity of items sold (weight) and profit. The rules mined without considering utility values (profit margin) will lead to a probable loss of profitable rules. The advantage of wealth of the...

Finding Influential Users in Social Media Using Association Rule Learning

Directory of Open Access Journals (Sweden)

Fredrik Erlandsson

2016-04-01

Full Text Available Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user participation. Normally, the success of a social media site is dependent on the activity level of the participating users. For both online social networking sites and individual users, it is of interest to find out if a topic will be interesting or not. In this article, we propose association learning to detect relationships between users. In order to verify the findings, several experiments were executed based on social network analysis, in which the most influential users identified from association rule learning were compared to the results from Degree Centrality and Page Rank Centrality. The results clearly indicate that it is possible to identify the most influential users using association rule learning. In addition, the results also indicate a lower execution time compared to state-of-the-art methods.

G2D: a tool for mining genes associated with disease

OpenAIRE

Perez-Iratxeta, Carolina; Wjst, Matthias; Bork, Peer; Andrade, Miguel A

2005-01-01

Abstract Background Human inherited diseases can be associated by genetic linkage with one or more genomic regions. The availability of the complete sequence of the human genome allows examining those locations for an associated gene. We previously developed an algorithm to prioritize genes on a chromosomal region according to their possible relation to an inherited disease using a combination of data mining on biomedical databases and gene sequence analysis. Results We have implemented this ...

Evolutionary Data Mining Approach to Creating Digital Logic

Science.gov (United States)

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

The legal aspects of the research and mining in the Brazilian law

International Nuclear Information System (INIS)

Godinho, T.M.

1982-01-01

The mining system main principles and concepts in Brazilian Legislation are presented, with description of the legislation that disciplines the activities of mineral research and mining emphasizing the special rules that guide the tasks of explotation, production and use of nuclear minerals and other minerals related to the nuclear area. (A.L.) [pt

DATA MINING UNTUK KLASIFIKASI PELANGGAN DENGAN ANT COLONY OPTIMIZATION

Directory of Open Access Journals (Sweden)

Maulani Kapiudin

2007-01-01

Full Text Available In this research the system for potentially customer classification is designed by extracting rule based classification from raw data with certain criteria. The searching process uses customer database from a bank with data mining technic by using ant colony optimization. A test based on min_case_per_rule variety and phenomene updating were done on a certain period of time. The result are group of customer class which base on rules built by ant and by modifying the pheromone updating, the area of the case is getting bigger. Prototype of the software is coded with C++ 6 version. The customer database master is created by using Microsoft Access. This paper gives information about potential customer of bank that can be classified by prototype of the software. Abstract in Bahasa Indonesia : Pada penelitian untuk sistem klasifikasi potensial customer ini didesain dengan melakukan ekstrak rule berdasarkan klasifikasi dari data mentah dengan kriteria tertentu. Proses pencarian menggunakan database pelanggan dari suatu bank dengan teknik data mining dengan ant colony optimization. Dilakukan percobaan dengan min_case_per_rule variety dan phenomene updating pada periode waktu tertentu. Hasilnya adalah sekelompok class pelanggan yang didasarkan dari rules yang dibangun dengan ant dan dengan dimodifikasi dengan pheromone updating, area permasalahan menjadi lebih melebar. Prototype dari software ini menggunakan C++ versi 6. Database pelanggan dibangun dengan Microsoft Access. Paper ini memberikan informasi mengenai potensi pelanggan dari bank, sehingga dapat diklasifikasikan dengan prototype dari software. Kata kunci: ant colony optimization, classification, min_case_per_rule, term, pheromone updating

Profiling high frequency accident locations using associations rules

OpenAIRE

GEURTS, Karolien; WETS, Geert; BRIJS, Tom; VANHOOF, Koen

2002-01-01

In Belgium, traffic safety is currently one of the government’s highest priorities. Identifying and profiling black spots and black zones in terms of accident related data and location characteristics must provide new insights into the complexity and causes of road accidents which, in turn, provide valuable input for government actions. In this paper, association rules are used to identify accident circumstances that frequently occur together at high frequency accident locations. Furthermore,...

Adverse health effects in Canada geese (Branta canadensis) associated with waste from zinc and lead mines in the Tri-State Mining District (Kansas, Oklahoma, and Missouri, USA).

Science.gov (United States)

van der Merwe, Deon; Carpenter, James W; Nietfeld, Jerome C; Miesner, John F

2011-07-01

Lead and zinc poisoning have been recorded in a variety of bird species, including migrating waterfowl such as Canada Geese (Branta canadensis), at sites contaminated with mine waste from lead and zinc mines in the Tri-State Mining District, Kansas, Oklahoma, and Missouri, USA. The adverse health impacts from mine waste on these birds may, however, be more extensive than is apparent from incidental reports of clinical disease. To characterize health impacts from mine waste on Canada Geese that do not have observable signs of poisoning, four to eight apparently healthy birds per site were collected from four contaminated sites and an uncontaminated reference site, and examined for physical and physiologic evidence of metals poisoning. Tissue concentrations of silver, aluminum, arsenic, barium, cadmium, cobalt, chromium, copper, iron, magnesium, manganese, molybdenum, nickel, lead, selenium, thallium, vanadium, and zinc were determined by inductively coupled plasma mass spectroscopy. Adverse health effects due to lead were characterized by assessing blood δ-aminolevulinic acid dehydratase (ALAD) enzyme activity. Adverse effects associated with zinc poisoning were determined from histologic examination of pancreas tissues. Elevated tissue lead concentrations and inhibited blood ALAD enzyme activities were consistently found in birds at all contaminated sites. Histopathologic signs of zinc poisoning, including fibrosis and vacuolization, were associated with elevated pancreatic zinc concentrations at one of the study sites. Adverse health effects associated with other analyzed elements, or tissue concentrations indicating potentially toxic exposure levels to these elements, were not observed.

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

Science.gov (United States)

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...

Mine subsidence control projects associated with solid waste disposal facilities

International Nuclear Information System (INIS)

Wood, R.M.

1994-01-01

Pennsylvania environmental regulations require applicant's for solid waste disposal permits to provide information regarding the extent of deep mining under the proposed site, evaluations of the maximum subsidence potential, and designs of measures to mitigate potential subsidence impact on the facility. This paper presents three case histories of deep mine subsidence control projects at solid waste disposal facilities. Each case history presents site specific mine grouting project data summaries which include evaluations of the subsurface conditions from drilling, mine void volume calculations, grout mix designs, grouting procedures and techniques, as well as grout coverage and extent of mine void filling evaluations. The case studies described utilized basic gravity grouting techniques to fill the mine voids and fractured strata over the collapsed portions of the deep mines. Grout mixtures were designed to achieve compressive strengths suitable for preventing future mine subsidence while maintaining high flow characteristics to penetrate fractured strata. Verification drilling and coring was performed in the grouted areas to determine the extent of grout coverage and obtain samples of the in-place grout for compression testing. The case histories presented in this report demonstrate an efficient and cost effective technique for mine subsidence control projects

Development and application of an interaction network ontology for literature mining of vaccine-associated gene-gene interactions.

Science.gov (United States)

Hur, Junguk; Özgür, Arzucan; Xiang, Zuoshuang; He, Yongqun

2015-01-01

Literature mining of gene-gene interactions has been enhanced by ontology-based name classifications. However, in biomedical literature mining, interaction keywords have not been carefully studied and used beyond a collection of keywords. In this study, we report the development of a new Interaction Network Ontology (INO) that classifies >800 interaction keywords and incorporates interaction terms from the PSI Molecular Interactions (PSI-MI) and Gene Ontology (GO). Using INO-based literature mining results, a modified Fisher's exact test was established to analyze significantly over- and under-represented enriched gene-gene interaction types within a specific area. Such a strategy was applied to study the vaccine-mediated gene-gene interactions using all PubMed abstracts. The Vaccine Ontology (VO) and INO were used to support the retrieval of vaccine terms and interaction keywords from the literature. INO is aligned with the Basic Formal Ontology (BFO) and imports terms from 10 other existing ontologies. Current INO includes 540 terms. In terms of interaction-related terms, INO imports and aligns PSI-MI and GO interaction terms and includes over 100 newly generated ontology terms with 'INO_' prefix. A new annotation property, 'has literature mining keywords', was generated to allow the listing of different keywords mapping to the interaction types in INO. Using all PubMed documents published as of 12/31/2013, approximately 266,000 vaccine-associated documents were identified, and a total of 6,116 gene-pairs were associated with at least one INO term. Out of 78 INO interaction terms associated with at least five gene-pairs of the vaccine-associated sub-network, 14 terms were significantly over-represented (i.e., more frequently used) and 17 under-represented based on our modified Fisher's exact test. These over-represented and under-represented terms share some common top-level terms but are distinct at the bottom levels of the INO hierarchy. The analysis of these

Natural radioactivity associated with bituminous coal mining in Nigeria

International Nuclear Information System (INIS)

Balogun, F.A.; Mokobia, C.E.; Fasasi, M.K.; Ogundare, F.O.

2003-01-01

Gamma spectroscopic method was used to determine the natural radioactivity associated with the mining of Nigerian bituminous coal for the purpose of determining the radiological implications of coal mining in the country. The activity concentrations of the radionuclides detected range from 0.20±0.002 to 48.42±5.32 Bq kg -1 . The overall natural radionuclide contribution to the radioactivity of the environment was found to be 404.16±23.44 Bq kg -1 . Of this, coal waste (tailing) alone contributed 49.5% representing the largest contribution. Coal contributed just 5.5%. A comparison of the concentrations obtained in this work for coal with those from other parts of the world indicates that the radioactivity content of the Nigerian bituminous coal is not significantly different. The outdoor and indoor exposure rates in air 1 m above the ground are estimated to be (6.31±1.20)x10 -8 and (7.57±1.20) x10 -8 Gy h -1 , respectively, for the mining environment. These values compare very well with the global values reported by UNSCEAR: 5x10 -8 and 6x10 -8 Gy h -1 , respectively. The resulting annual effective dose equivalent estimated is (4.49±0.74)x10 -4 Sv yr -1 . This also compares favourably with the global value -4x10 -4 Sv yr -1 , reported by UNSCEAR

Employee motivation and work performance: A comparative study of mining companies in Ghana

Energy Technology Data Exchange (ETDEWEB)

Kuranchie-Mensah, E.; Amponsah-Tawiah, K.

2016-07-01

The paper empirically compares employee motivation and its impact on performance in Ghanaian Mining Companies, where in measuring performance, the job satisfaction model is used. The study employed exploratory research design in gathering data from four large-scale Gold mining companies in Ghana with regards to their policies and structures in the effectiveness of motivational tools and strategies used by these companies. The study observed that, due to the risk factors associated with the mining industry, management has to ensure that employees are well motivated to curb the rate at which employees embark on industrial unrest which affect performance, and employees are to comply with health and safety rules because the industry contribute hugely to the Gross Domestic Product (GDP) of the country. Limitation to the present study include the researcher’s inability to contact other mining companies. However, the study suggests possibilities for future research including contacting other mining companies, expanding the sample size, managers ensuring that the safety and health needs of staff are addressed particularly those exposed to toxic and harmful chemicals. A lot of studies have been done on mining companies in the past. This paper fills a gap perceived that employees in this sector are highly motivated in spite of the challenges being faced by them, and knowing more about what keeps employees moving is still of national interest. (Author)

Employee motivation and work performance: A comparative study of mining companies in Ghana

International Nuclear Information System (INIS)

Kuranchie-Mensah, E.; Amponsah-Tawiah, K.

2016-01-01

The paper empirically compares employee motivation and its impact on performance in Ghanaian Mining Companies, where in measuring performance, the job satisfaction model is used. The study employed exploratory research design in gathering data from four large-scale Gold mining companies in Ghana with regards to their policies and structures in the effectiveness of motivational tools and strategies used by these companies. The study observed that, due to the risk factors associated with the mining industry, management has to ensure that employees are well motivated to curb the rate at which employees embark on industrial unrest which affect performance, and employees are to comply with health and safety rules because the industry contribute hugely to the Gross Domestic Product (GDP) of the country. Limitation to the present study include the researcher’s inability to contact other mining companies. However, the study suggests possibilities for future research including contacting other mining companies, expanding the sample size, managers ensuring that the safety and health needs of staff are addressed particularly those exposed to toxic and harmful chemicals. A lot of studies have been done on mining companies in the past. This paper fills a gap perceived that employees in this sector are highly motivated in spite of the challenges being faced by them, and knowing more about what keeps employees moving is still of national interest. (Author)

artery disease guidelines with extracted knowledge from data mining

Directory of Open Access Journals (Sweden)

Peyman Rezaei-Hachesu

2017-06-01

Conclusion: Guidelines confirm the achieved results from data mining (DM techniques and help to rank important risk factors based on national and local information. Evaluation of extracted rules determined new patterns for CAD patients.

Arsenic pollution and fractionation in sediments and mine waste samples from different mine sites

International Nuclear Information System (INIS)

Larios, Raquel; Fernández-Martínez, Rodolfo; Álvarez, Rodrigo; Rucandio, Isabel

2012-01-01

A characterization of arsenic pollution and its associations with solid mineral phases in sediments and spoil heap samples from four different abandoned mines in Spain is performed. Three of them were mercury mines located in the same mining district, in the province of Asturias, and the other one, devoted to arsenic mining, is in the province of León. A sequential extraction procedure, especially developed for arsenic, was applied for the study of arsenic partitioning. Very high total arsenic concentrations ranging 300–67,000 mg·kg −1 were found. Arsenic fractionation in each mine is broadly in accordance with the mineralogy of the area and the extent of the mine workings. In almost all the studied samples, arsenic appeared predominantly associated with iron oxyhydroxides, especially in the amorphous form. Sediments from cinnabar roasted piles showed a higher arsenic mobility as a consequence of an intense ore treatment, posing an evident risk of arsenic spread to the surroundings. Samples belonging to waste piles where the mining activity was less intense presented a higher proportion of arsenic associated with structural minerals. Nevertheless, it represents a long-term source of arsenic to the environment. - Highlights: ► Arsenic fractionation in sediments from different mining areas is evaluated. ► A sequential extraction scheme especially designed for arsenic partitioning is applied. ► As associations with mineral pools is in accordance to the mineralogy of each area. ► As distribution and mobility in each area depends on the extent of mining activity. ► As occurs mainly associated with amorphous iron oxyhydroxides in all samples.

Arsenic pollution and fractionation in sediments and mine waste samples from different mine sites

Energy Technology Data Exchange (ETDEWEB)

Larios, Raquel; Fernandez-Martinez, Rodolfo [Unidad de Espectroscopia, Division de Quimica, Departamento de Tecnologia, CIEMAT. Av. Complutense, 40, E-28040 Madrid (Spain); Alvarez, Rodrigo [Dpto. de Explotacion y Prospeccion de Minas, Universidad de Oviedo, ETS de Ingenieros de Minas, C/Independencia, 13, E-33004 Oviedo (Spain); Rucandio, Isabel, E-mail: isabel.rucandio@ciemat.es [Unidad de Espectroscopia, Division de Quimica, Departamento de Tecnologia, CIEMAT. Av. Complutense, 40, E-28040 Madrid (Spain)

2012-08-01

A characterization of arsenic pollution and its associations with solid mineral phases in sediments and spoil heap samples from four different abandoned mines in Spain is performed. Three of them were mercury mines located in the same mining district, in the province of Asturias, and the other one, devoted to arsenic mining, is in the province of Leon. A sequential extraction procedure, especially developed for arsenic, was applied for the study of arsenic partitioning. Very high total arsenic concentrations ranging 300-67,000 mg{center_dot}kg{sup -1} were found. Arsenic fractionation in each mine is broadly in accordance with the mineralogy of the area and the extent of the mine workings. In almost all the studied samples, arsenic appeared predominantly associated with iron oxyhydroxides, especially in the amorphous form. Sediments from cinnabar roasted piles showed a higher arsenic mobility as a consequence of an intense ore treatment, posing an evident risk of arsenic spread to the surroundings. Samples belonging to waste piles where the mining activity was less intense presented a higher proportion of arsenic associated with structural minerals. Nevertheless, it represents a long-term source of arsenic to the environment. - Highlights: Black-Right-Pointing-Pointer Arsenic fractionation in sediments from different mining areas is evaluated. Black-Right-Pointing-Pointer A sequential extraction scheme especially designed for arsenic partitioning is applied. Black-Right-Pointing-Pointer As associations with mineral pools is in accordance to the mineralogy of each area. Black-Right-Pointing-Pointer As distribution and mobility in each area depends on the extent of mining activity. Black-Right-Pointing-Pointer As occurs mainly associated with amorphous iron oxyhydroxides in all samples.

Atmospheric particulate matter size distribution and concentration in West Virginia coal mining and non-mining areas.

Science.gov (United States)

Kurth, Laura M; McCawley, Michael; Hendryx, Michael; Lusk, Stephanie

2014-07-01

People who live in Appalachian areas where coal mining is prominent have increased health problems compared with people in non-mining areas of Appalachia. Coal mines and related mining activities result in the production of atmospheric particulate matter (PM) that is associated with human health effects. There is a gap in research regarding particle size concentration and distribution to determine respiratory dose around coal mining and non-mining areas. Mass- and number-based size distributions were determined with an Aerodynamic Particle Size and Scanning Mobility Particle Sizer to calculate lung deposition around mining and non-mining areas of West Virginia. Particle number concentrations and deposited lung dose were significantly greater around mining areas compared with non-mining areas, demonstrating elevated risks to humans. The greater dose was correlated with elevated disease rates in the West Virginia mining areas. Number concentrations in the mining areas were comparable to a previously documented urban area where number concentration was associated with respiratory and cardiovascular disease.

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

Science.gov (United States)

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.

Management of mining-related damages in abandoned underground coal mine areas using GIS

International Nuclear Information System (INIS)

Lee, U.J.; Kim, J.A.; Kim, S.S.; Kim, W.K.; Yoon, S.H.; Choi, J.K.

2005-01-01

The mining-related damages such as ground subsidence, acid mine drainage (AMD), and deforestation in the abandoned underground coal mine areas become an object of public concern. Therefore, the system to manage the mining-related damages is needed for the effective drive of rehabilitation activities. The management system for Abandoned Underground Coal Mine using GIS includes the database about mining record and information associated with the mining-related damages and application programs to support mine damage prevention business. Also, this system would support decision-making policy for rehabilitation and provide basic geological data for regional construction works in abandoned underground coal mine areas. (authors)

HIV preventive behavior and associated factors among mining workers in Sali traditional gold mining site Bench Maji zone, Southwest Ethiopia: a cross sectional study.

Science.gov (United States)

Abdissa, Hordofa Gutema; Lemu, Yohannes Kebede; Nigussie, Dejene Tilahun

2014-09-26

Prevalence of HIV and other STI is high among migrant mining workers due to factors such as dangerous working conditions, only masculine identities existence, living away from families, desolate and in hospitable place. This makes them known to be HIV and STI vulnerable group in different part of the world. But, in Ethiopia they were not thought as at risk group yet. So the aim of this study is to assess magnitude of HIV preventive behaviours and associated factors among gold miners in Sali traditional gold mining site. A cross sectional study was conducted to assess HIV preventive behavior of the mining worker. The data were collected using interviewer administered structured questionnaire adapted from other related behavioural studies. The data was entered using EPI data version 3.1 and analyzed using SPSS version 17. Multiple logistic regression was used to assess relationship of HIV preventive behavior with constructs of health belief model. A total of 393 respondents with response rate of 93.12% were participated. All of the study participants were male 393(100%), the mean age of the participant was 24.0 (± 5.13SD). Less than half of the respondents 187(47.6%) were engaged in HIV preventive behavior. Less than half (45.3%) of them have high perceived susceptibility to HIV/AIDS; majority (62.8%) of them has high perceived severity to HIV/AIDS. HIV preventive behavior is negatively associated with being in middle, higher and highest income [OR = 0.54, 95% CI: 0.21, 0.74], [OR = 0.40, 95% CI: 0.30, 0.98] and [OR = 0.39, 95% CI: 0.20, 0.77] respectively and positively associated with Completing secondary, tertiary school and self efficacy [OR = 2.66, 95% CI: 1.11, 6.41], [OR = 5.40, 95% CI: 1.54, 19] and [OR = 1.88, 95% CI: 1.18, 2.94] respectively. The HIV preventive behavior of the mining worker was low. Being engaged in sexual intercourse with one sexual partner is very low, Consistent condom use among these mining workers was low. Income, educational status

Environmental risks associated to wind erosion in a metal mining area from SE Spain

International Nuclear Information System (INIS)

Garcia Fernandez, G.; Romero Diaz, A.

2009-01-01

Soils and mining wastes from the Mediterranean mining area placed in the Sierra Minera Mountains are highly enriched in heavy metals such as lead and zinc, but also other metals such as cadmium and arsenic. Wind erosion in this area could be considered extremely high and hazards associated to this eroded sediments seems to be high because the huge amount of metals present in this wastes. Therefore, combination of high erosion rates and high metal concentration in this mining waste, make those environmental risks can be considered high for the surrounding ecosystems, but also for public health of the nearby villages and towns. In order, to study these wind erosion processes over these mining materials, some experiments for the evaluation of the transportation of soil particles were carried out. Erosion rates in this realm is particularly important during spring months, when increased activity of the eastern winds brings intense soil dragging, with strong effects on the metals dispersion, including the massive removal of sediments. (Author) 16 refs.

Environmental risks associated to wind erosion in a metal mining area from SE Spain

Energy Technology Data Exchange (ETDEWEB)

Garcia Fernandez, G.; Romero Diaz, A.

2009-07-01

Soils and mining wastes from the Mediterranean mining area placed in the Sierra Minera Mountains are highly enriched in heavy metals such as lead and zinc, but also other metals such as cadmium and arsenic. Wind erosion in this area could be considered extremely high and hazards associated to this eroded sediments seems to be high because the huge amount of metals present in this wastes. Therefore, combination of high erosion rates and high metal concentration in this mining waste, make those environmental risks can be considered high for the surrounding ecosystems, but also for public health of the nearby villages and towns. In order, to study these wind erosion processes over these mining materials, some experiments for the evaluation of the transportation of soil particles were carried out. Erosion rates in this realm is particularly important during spring months, when increased activity of the eastern winds brings intense soil dragging, with strong effects on the metals dispersion, including the massive removal of sediments. (Author) 16 refs.

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

Science.gov (United States)

2011-03-01

... DEPARTMENT OF LABOR Mine Safety and Health Administration 30 CFR Part 75 RIN 1219-AB75 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...

Statistical methods of estimating mining costs

Science.gov (United States)

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.

Data Mining Relationships Among Urban Socioeconomic, Land Cover, and Remotely Sensed Ecological Data

Science.gov (United States)

Mennis, J.; Wessman, C.; Golubiewski, N.

2003-12-01

This research investigates the relationships among socioeconomic character, land cover, and ecological function in a rapidly urbanizing region, the Front Range of Colorado. We use novel spatial geographic information systems- (GIS-) based data integration and data mining techniques to integrate and analyze diverse spatial data sets. These data include elevation data, transportation data, land cover data derived from aerial photography, block group-level U.S. Census data, and vegetation greenness (NDVI) data derived from Landsat imagery. These data are used to derive a variety of U.S. block group-level variables indicating demographic, geographic, ecological, and land cover characteristics. We employ spatial association rule mining, decision tree induction, and spatial on-line analytical processing (OLAP), in addition to more conventional multivariate statistical techniques, to investigate relationships among these variables.

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

Science.gov (United States)

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

Self-regulation in the mining industry

DEFF Research Database (Denmark)

Sinding, Knud; Peck, Philip

2013-01-01

Many industries have established their own systems for self-regulation. They often do so when companies involved in the industry operate in countries where financial, technical, environmental and social regulation is weak and when the industry is challenged by legitimacy issues related to behaviour...... in one of these areas. One industry that has progressed unevenly down the road of self-regulation in these areas is mining. Developing self-regulation for mines and mining companies involves difficult questions of scope, rules, membership, assessment criteria and performance evaluation. While self-regulation...... may bring benefits to members, they are likely to take a long time coming; but when they do arrive they may be substantial. Using a range of theoretical and empirical results from research on self-regulation, performance rating and corporate strategy, this paper analyses the strategic and operational...

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

Directory of Open Access Journals (Sweden)

Saurav Mallik

2017-12-01

Full Text Available 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.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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…

Mining-induced fault reactivation associated with the main conveyor belt roadway and safety of the Barapukuria Coal Mine in Bangladesh: Constraints from BEM simulations

Energy Technology Data Exchange (ETDEWEB)

Islam, Md. Rafiqul; Shinjo, Ryuichi [Department of Physics and Earth Sciences, University of the Ryukyus, Okinawa, 903-0213 (Japan)

2009-09-01

Fault reactivation during underground mining is a critical problem in coal mines worldwide. This paper investigates the mining-induced reactivation of faults associated with the main conveyor belt roadway (CBR) of the Barapukuria Coal Mine in Bangladesh. The stress characteristics and deformation around the faults were investigated by boundary element method (BEM) numerical modeling. The model consists of a simple geometry with two faults (Fb and Fb1) near the CBR and the surrounding rock strata. A Mohr-Coulomb failure criterion with bulk rock properties is applied to analyze the stability and safety around the fault zones, as well as for the entire mining operation. The simulation results illustrate that the mining-induced redistribution of stresses causes significant deformation within and around the two faults. The horizontal and vertical stresses influence the faults, and higher stresses are concentrated near the ends of the two faults. Higher vertical tensional stress is prominent at the upper end of fault Fb. High deviatoric stress values that concentrated at the ends of faults Fb and Fb1 indicate the tendency towards block failure around the fault zones. The deviatoric stress patterns imply that the reinforcement strength to support the roof of the roadway should be greater than 55 MPa along the fault core zone, and should be more than 20 MPa adjacent to the damage zone of the fault. Failure trajectories that extend towards the roof and left side of fault Fb indicate that mining-induced reactivation of faults is not sufficient to generate water inflow into the mine. However, if movement of strata occurs along the fault planes due to regional earthquakes, and if the faults intersect the overlying Lower Dupi Tila aquiclude, then liquefaction could occur along the fault zones and enhance water inflow into the mine. The study also reveals that the hydraulic gradient and the general direction of groundwater flow are almost at right angles with the trends of

Communications construction on mining grounds influenced by mining damage. Budownictwo komunikacyjne na terenach objetych szkodami gorniczymi

Energy Technology Data Exchange (ETDEWEB)

Rosikon, A

1979-01-01

This book considers problems associated with construction of communication lines on grounds influenced by underground coal mining. It is stated that about 50% of coal mined in Poland comes from protective coal pillars. Improving methods of strata control and ground control after underground mining will influence perspectives of mining in protective pillars. The following problems associated with minimizing mining damage are analyzed: types of ground deformation caused by underground mining, continuous and discontinuous deformation, factors which influence formation of subsidence troughs, forecasting ground subsidence according to the Knothe and Budryk theory, horizontal and vertical ground dislocation, coefficients used for description of ground deformation, Kochmanski's theory of continuous deformation, effects of ground subsidence of foundations of buildings and industrial structures, construction of roads, railway tracks and other communication lines on ground influenced by discontinuous deformations caused by coal mining, problems associated with construction of bridges and tunnels, construction of sewage systems, effects of underground mining on maintenance and repair of communication lines and sewage systems. Ways of minimizing discontinuous ground deformation are analyzed.

Educational Data Mining Application for Estimating Students Performance in Weka Environment

Science.gov (United States)

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.

Restoring Forests and Associated Ecosystem Services on Appalachian Coal Surface Mines

Science.gov (United States)

Zipper, Carl E.; Burger, James A.; Skousen, Jeffrey G.; Angel, Patrick N.; Barton, Christopher D.; Davis, Victor; Franklin, Jennifer A.

2011-05-01

Surface coal mining in Appalachia has caused extensive replacement of forest with non-forested land cover, much of which is unmanaged and unproductive. Although forested ecosystems are valued by society for both marketable products and ecosystem services, forests have not been restored on most Appalachian mined lands because traditional reclamation practices, encouraged by regulatory policies, created conditions poorly suited for reforestation. Reclamation scientists have studied productive forests growing on older mine sites, established forest vegetation experimentally on recent mines, and identified mine reclamation practices that encourage forest vegetation re-establishment. Based on these findings, they developed a Forestry Reclamation Approach (FRA) that can be employed by coal mining firms to restore forest vegetation. Scientists and mine regulators, working collaboratively, have communicated the FRA to the coal industry and to regulatory enforcement personnel. Today, the FRA is used routinely by many coal mining firms, and thousands of mined hectares have been reclaimed to restore productive mine soils and planted with native forest trees. Reclamation of coal mines using the FRA is expected to restore these lands' capabilities to provide forest-based ecosystem services, such as wood production, atmospheric carbon sequestration, wildlife habitat, watershed protection, and water quality protection to a greater extent than conventional reclamation practices.

Comparison of Heuristics for Inhibitory Rule Optimization

KAUST Repository

Alsolami, Fawaz

2014-09-13

Knowledge representation and extraction are very important tasks in data mining. In this work, we proposed a variety of rule-based greedy algorithms that able to obtain knowledge contained in a given dataset as a series of inhibitory rules containing an expression “attribute ≠ value” on the right-hand side. The main goal of this paper is to determine based on rule characteristics, rule length and coverage, whether the proposed rule heuristics are statistically significantly different or not; if so, we aim to identify the best performing rule heuristics for minimization of rule length and maximization of rule coverage. Friedman test with Nemenyi post-hoc are used to compare the greedy algorithms statistically against each other for length and coverage. The experiments are carried out on real datasets from UCI Machine Learning Repository. For leading heuristics, the constructed rules are compared with optimal ones obtained based on dynamic programming approach. The results seem to be promising for the best heuristics: the average relative difference between length (coverage) of constructed and optimal rules is at most 2.27% (7%, respectively). Furthermore, the quality of classifiers based on sets of inhibitory rules constructed by the considered heuristics are compared against each other, and the results show that the three best heuristics from the point of view classification accuracy coincides with the three well-performed heuristics from the point of view of rule length minimization.

Heavy metal pollution associated with an abandoned lead-zinc mine in the Kirki region, NE Greece.

Science.gov (United States)

Nikolaidis, Christos; Zafiriadis, Ilias; Mathioudakis, Vasileios; Constantinidis, Theodore

2010-09-01

The "Agios Philippos" mine in the Kirki region (NE Greece) has been abandoned in 1998 after half a century of ore exploration without a reclamation or remediation plan. This article aims at elucidating the potential environmental risks associated with this site by quantifying pollution in tailing basins, stream waters, stream sediments and agricultural fields. Concentrations of heavy metals in the abandoned mine tailings reached 12,567 mg/kg for Pb, 22,292 mg/kg for Zn, 174 mg/kg for Cd and 241 mg/kg for As. The geoaccumulation index and enrichment factor for these metals were indicative of extremely high contamination (I(geo) > 5) and extremely high enrichment (EF > 40), respectively. Stream waters in the proximity of the mine had an acidic pH equal to 5.96 and a high sulfate content (SO(4)(-2) = 545.5 mg/L), whereas concentrations of Mn, Zn and Cd reached 2,399 microg/L, 7,681 microg/L and 11.2 microg/L. High I(geo) and EF values for Cd, Zn and As in stream sediments indicates that surface water pollution has a historic background, which is typically associated with acid mine drainage. Agricultural fields in the proximity of the mine exhibited high I(geo) and EF values, which were in decreasing order Cd > Pb > Zn > As. These findings urge for an immediate remediation action of the afflicted area.

75 FR 17511 - Coal Mine Dust Sampling Devices

Science.gov (United States)

2010-04-06

... base and free to oscillate at its narrow or free end on which the collection filter is mounted... coal mines from power stations, electric motors and remote control transmitters. The final rule... electromagnetic interference. The FCC is an independent Federal agency that regulates radiofrequency emitting...

Improve Data Mining and Knowledge Discovery through the use of MatLab

Science.gov (United States)

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

Mining Contratação License in the New Regulatory Framework of Brazilian Mining: Some Notes on the Institutes of Research Authorization and Mining Concession

Directory of Open Access Journals (Sweden)

Adhemar Ronquim Filho

2013-06-01

Full Text Available Given the importance of mining nowadays, Government seeks ways to stimulate its growth, focusing on potentializing research and the advancement of mineral processing, the basic items for speeding up this activity in a profitable way. In this sense, the discussions on the crystallization of a new regulatory framework for the Brazilian mining have been deepened and, despite gathering a significant number of proposals, it does not have a closed text, and, currently, it is far from obtaining an approval or a final word (despite the urgency. However, the analysis of the proposals that have been presented reveals that there is an intention to institute new rules for the modernization of Brazilian mining, and this paper has the purpose of suggesting ways to reconcile conflicts permeated by various dissonant interests that surround the Brazilian mining at this time. It should be emphasized that, given the lack of official disclosure of the amendments proposed, the approach will continue limited to what has been released by MME (Ministry of Mines and Energy and by the studies that have already been presented by experts in the field (connected to government and/or private businesses. It is restricted to discuss changes to be implemented with the new regulatory framework, highlighting points to be observed, and, among the topics that require mandatory update, we can emphasize the changes in the procedures of exploration permits and mining.

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

Science.gov (United States)

2010-05-20

...-AP48 National Emission Standards for Hazardous Air Pollutants: Gold Mine Ore Processing and Production... published a proposed rule for mercury emissions from the gold mine ore processing and production area source... Environmental protection, Air pollution control, Hazardous substances, Incorporations by reference, Reporting...

78 FR 65589 - Leasing of Osage Reservation Lands for Oil and Gas Mining

Science.gov (United States)

2013-11-01

... DEPARTMENT OF THE INTERIOR Bureau of Indian Affairs 25 CFR Part 226 [BIA-2013-0003; 134/A0A511010/AAK1001000] RIN 1076-AF17 Leasing of Osage Reservation Lands for Oil and Gas Mining AGENCY: Bureau of Indian... oil and gas mining on reservation land of the Osage Nation. The public comment period for that rule...

DiMeX: A Text Mining System for Mutation-Disease Association Extraction.

Science.gov (United States)

Mahmood, A S M Ashique; Wu, Tsung-Jung; Mazumder, Raja; Vijay-Shanker, K

2016-01-01

The number of published articles describing associations between mutations and diseases is increasing at a fast pace. There is a pressing need to gather such mutation-disease associations into public knowledge bases, but manual curation slows down the growth of such databases. We have addressed this problem by developing a text-mining system (DiMeX) to extract mutation to disease associations from publication abstracts. DiMeX consists of a series of natural language processing modules that preprocess input text and apply syntactic and semantic patterns to extract mutation-disease associations. DiMeX achieves high precision and recall with F-scores of 0.88, 0.91 and 0.89 when evaluated on three different datasets for mutation-disease associations. DiMeX includes a separate component that extracts mutation mentions in text and associates them with genes. This component has been also evaluated on different datasets and shown to achieve state-of-the-art performance. The results indicate that our system outperforms the existing mutation-disease association tools, addressing the low precision problems suffered by most approaches. DiMeX was applied on a large set of abstracts from Medline to extract mutation-disease associations, as well as other relevant information including patient/cohort size and population data. The results are stored in a database that can be queried and downloaded at http://biotm.cis.udel.edu/dimex/. We conclude that this high-throughput text-mining approach has the potential to significantly assist researchers and curators to enrich mutation databases.

Injury Profiles Associated with Artisanal and Small-Scale Gold Mining in Tarkwa, Ghana.

Science.gov (United States)

Calys-Tagoe, Benedict N L; Ovadje, Lauretta; Clarke, Edith; Basu, Niladri; Robins, Thomas

2015-07-10

Artisanal and small-scale gold mining (ASGM) is inherently risky, but little is known about mining-associated hazards and injuries despite the tremendous growth worldwide of ASGM and the benefits it offers. The current study aimed to characterize the physical injuries associated with ASGM in Ghana to guide policy formulation. A cross-sectional survey was carried out in the Tarkwa mining district of the Western Region of Ghana in 2014. A total of 404 small-scale miners were recruited and interviewed regarding their occupational injury experiences over the preceding 10 years using a paper-based structured questionnaire. Nearly one-quarter (23.5%) of the miners interviewed reported getting injured over the previous 10 years, and the overall injury rate was calculated to be 5.39 per 100 person years. The rate was significantly higher for women (11.93 per 100 person years) and those with little mining experience (e.g., 25.31 per 100 person years for those with less than one year of work experience). The most injury-prone mining activities were excavation (58.7%) and crushing (23.1%), and over 70% of the injuries were reported to be due to miners being hit by an object. The majority of the injuries (57%) were lacerations, and nearly 70% of the injuries were to the upper or lower limbs. Approximately one-third (34.7%) of the injuries resulted in miners missing more than two weeks of work. One-quarter of the injured workers believed that abnormal work pressure played a role in their injuries, and nearly two-fifths believed that their injuries could have been prevented, with many citing personal protective equipment as a solution. About one-quarter of the employees reported that their employers never seemed to be interested in the welfare or safety of their employees. These findings greatly advance our understanding of occupational hazards and injuries amongst ASGM workers and help identify several intervention points.

Employee motivation and work performance: A comparative study of mining companies in Ghana

Directory of Open Access Journals (Sweden)

Elizabeth Boye Kuranchie-Mensah

2016-04-01

Full Text Available Purpose: The paper empirically compares employee motivation and its impact on performance in Ghanaian Mining Companies, where in measuring performance, the job satisfaction model is used. Design/methodology/approach: The study employed exploratory research design in gathering data from four large-scale Gold mining companies in Ghana with regards to their policies and structures in the effectiveness of motivational tools and strategies used by these companies. Findings: The study observed that, due to the risk factors associated with the mining industry, management has to ensure that employees are well motivated to curb the rate at which employees embark on industrial unrest which affect performance, and employees are to comply with health and safety rules because the industry contribute hugely to the Gross Domestic Product (GDP of the country. Research Limitations/Implications: Limitation to the present study include the researcher’s inability to contact other mining companies. However, the study suggests possibilities for future research including contacting other mining companies, expanding the sample size, managers ensuring that the safety and health needs of staff are addressed particularly those exposed to toxic and harmful chemicals. Originality/Value: A lot of studies have been done on mining companies in the past. This paper fills a gap perceived that employees in this sector are highly motivated in spite of the challenges being faced by them, and knowing more about what keeps employees moving is still of national interest.

Design of data warehouse in teaching state based on OLAP and data mining

Science.gov (United States)

Zhou, Lijuan; Wu, Minhua; Li, Shuang

2009-04-01

The data warehouse and the data mining technology is one of information technology research hot topics. At present the data warehouse and the data mining technology in aspects and so on commercial, financial industry as well as enterprise's production, market marketing obtained the widespread application, but is relatively less in educational fields' application. Over the years, the teaching and management have been accumulating large amounts of data in colleges and universities, while the data can not be effectively used, in the light of social needs of the university development and the current status of data management, the establishment of data warehouse in university state, the better use of existing data, and on the basis dealing with a higher level of disposal --data mining are particularly important. In this paper, starting from the decision-making needs design data warehouse structure of university teaching state, and then through the design structure and data extraction, loading, conversion create a data warehouse model, finally make use of association rule mining algorithm for data mining, to get effective results applied in practice. Based on the data analysis and mining, get a lot of valuable information, which can be used to guide teaching management, thereby improving the quality of teaching and promoting teaching devotion in universities and enhancing teaching infrastructure. At the same time it can provide detailed, multi-dimensional information for universities assessment and higher education research.

Decree 2006-265/PRN of 18 August 2006 fixing the modalities of mining law application

International Nuclear Information System (INIS)

2006-01-01

This decree fixes modalities of applying ordinance 93-16 of 2 march 1993 concerning mining law in Niger Republic and its subsequent modified text. Any petitioner, owner of mining title, prospecting authorization, opening and mining quarry, sub-leaser shall have an office in Niger Republic and notify it to the Minister of Mines and energy. each licence or lease is based on an agreement between the government and the society. Any change of status, capital or personnel of the company shall be noted to the Minister of Mines and energy. The company shall pay fiscal duties and respect rules and regulations concerning mines and quarries health and safety [fr

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

Science.gov (United States)

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.

Text mining facilitates database curation - extraction of mutation-disease associations from Bio-medical literature.

Science.gov (United States)

Ravikumar, Komandur Elayavilli; Wagholikar, Kavishwar B; Li, Dingcheng; Kocher, Jean-Pierre; Liu, Hongfang

2015-06-06

Advances in the next generation sequencing technology has accelerated the pace of individualized medicine (IM), which aims to incorporate genetic/genomic information into medicine. One immediate need in interpreting sequencing data is the assembly of information about genetic variants and their corresponding associations with other entities (e.g., diseases or medications). Even with dedicated effort to capture such information in biological databases, much of this information remains 'locked' in the unstructured text of biomedical publications. There is a substantial lag between the publication and the subsequent abstraction of such information into databases. Multiple text mining systems have been developed, but most of them focus on the sentence level association extraction with performance evaluation based on gold standard text annotations specifically prepared for text mining systems. We developed and evaluated a text mining system, MutD, which extracts protein mutation-disease associations from MEDLINE abstracts by incorporating discourse level analysis, using a benchmark data set extracted from curated database records. MutD achieves an F-measure of 64.3% for reconstructing protein mutation disease associations in curated database records. Discourse level analysis component of MutD contributed to a gain of more than 10% in F-measure when compared against the sentence level association extraction. Our error analysis indicates that 23 of the 64 precision errors are true associations that were not captured by database curators and 68 of the 113 recall errors are caused by the absence of associated disease entities in the abstract. After adjusting for the defects in the curated database, the revised F-measure of MutD in association detection reaches 81.5%. Our quantitative analysis reveals that MutD can effectively extract protein mutation disease associations when benchmarking based on curated database records. The analysis also demonstrates that incorporating

Mining pathway associations for disease-related pathway activity analysis based on gene expression and methylation data.

Science.gov (United States)

Lee, Hyeonjeong; Shin, Miyoung

2017-01-01

The problem of discovering genetic markers as disease signatures is of great significance for the successful diagnosis, treatment, and prognosis of complex diseases. Even if many earlier studies worked on identifying disease markers from a variety of biological resources, they mostly focused on the markers of genes or gene-sets (i.e., pathways). However, these markers may not be enough to explain biological interactions between genetic variables that are related to diseases. Thus, in this study, our aim is to investigate distinctive associations among active pathways (i.e., pathway-sets) shown each in case and control samples which can be observed from gene expression and/or methylation data. The pathway-sets are obtained by identifying a set of associated pathways that are often active together over a significant number of class samples. For this purpose, gene expression or methylation profiles are first analyzed to identify significant (active) pathways via gene-set enrichment analysis. Then, regarding these active pathways, an association rule mining approach is applied to examine interesting pathway-sets in each class of samples (case or control). By doing so, the sets of associated pathways often working together in activity profiles are finally chosen as our distinctive signature of each class. The identified pathway-sets are aggregated into a pathway activity network (PAN), which facilitates the visualization of differential pathway associations between case and control samples. From our experiments with two publicly available datasets, we could find interesting PAN structures as the distinctive signatures of breast cancer and uterine leiomyoma cancer, respectively. Our pathway-set markers were shown to be superior or very comparable to other genetic markers (such as genes or gene-sets) in disease classification. Furthermore, the PAN structure, which can be constructed from the identified markers of pathway-sets, could provide deeper insights into

Chapter 16: text mining for translational bioinformatics.

Science.gov (United States)

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.

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

Science.gov (United States)

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.

National States, Environmental Conflicts and Mining in Latin America

Directory of Open Access Journals (Sweden)

Gabriela Scotto

2013-11-01

Full Text Available The work asks about possible factors that may explain the vertiginous increase, in the last decade, of social conflicts around mining in all Latin American countries that have mining activities. Is it possible to talk about "regional trends" in terms of blooming and development of these conflicts along the continent? If this is the case, which would be the trends? I aim to show that, beyond the consequences of great increase of external investments in mining sector, it is fundamental to look at the role of national states and analyze some of the main changes which occurred in national legislation dedicated to "rule" the property, access and exploitation of mineral resources.

Influence of Government economic policies on mining legislation

Energy Technology Data Exchange (ETDEWEB)

Jakob, K F

1980-01-01

As we know from experience, the relation between state and economy is characterized by more or less strong tensions. The following three groups have always claimed the right to dispose of mineral resources: the state - in former times the sovereign -, the landowners, and the mining industry. The first one has based his claim on his official power and has taken the view that he alone could protect the interests of the general public in winning mineral resources. The second ones have relied on their titles to real estates which basically cover unlimited depth. With the intent to work the mines, the mining industry refers to its know-how, performance and readiness, thus alleging that they would serve the national economy best. The historical development of mining laws has finally been characterized by a shift in priorities within these naturally strained relations which exist between state, landowners, and mining industry. It is examined how the emphasis has been shifted in this relationship in the course of time, with special consideration of the relationship between state and mining industry. Which rules of law the legislator intends to make with regard to mining laws will always depend on the question as to which economic policy the state intends to pursue.

Mining PubMed for Biomarker-Disease Associations to Guide Discovery

OpenAIRE

Jessen, Walter; Landschulz, Katherine; Turi, Thomas; Reams, Rachel

2012-01-01

Biomedical knowledge is growing exponentially; however, meta-knowledge around the data is often lacking. PubMed is a database comprising more than 21 million citations for biomedical literature from MEDLINE and additional life science journals dating back to the 1950s. To explore the use and frequency of biomarkers across human disease, we mined PubMed for biomarker-disease associations. We then ranked the top 100 linked diseases by relevance and mapped them to medical subject headings (MeSH)...

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

Science.gov (United States)

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...

Disputes over land and water rights in gold mining

NARCIS (Netherlands)

Stoltenborg, Didi; Boelens, Rutgerd

2016-01-01

This article analyzes different visions and positions in a conflict between the developer of an open-pit mine in Mexico and project opponents using the echelons of rights analysis framework, distinguishing four layers of dispute: contested resources; contents of rules and regulations;

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

Science.gov (United States)

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.

NIOSH (National Institute for Occupational Safety and Health) testimony to Department of Labor on the Mine Safety and Health Administration proposed rule: ionizing radiation standards for metal and nonmetal mines, August 13, 1987 by R. Niemeier

International Nuclear Information System (INIS)

1987-01-01

Recommendations were offered for protecting workers against the health effects of ionizing radiation in metal and nonmetal mines. Available data demonstrating such health effects was reviewed and evidence supporting the technical feasibility of reducing the current Mine Safety and Health Administration (MSHA) standard was presented. Five recent studies indicated a significant increase in lung cancer rates associated with radon progeny exposure in underground mines. Additional studies indicated an exposure/response relationship in uranium miners. The influence of smoking on the association between radon progeny exposure and lung cancer was cited. Evidence has indicated that exposure to radon progeny carries a potential risk of developing occupationally induced lung cancer. Risk-assessment data supported the conclusion that miners with the same characteristics as the United States Public Health Service uranium miners cohort and who accrue a cumulative occupational exposure of 120 working level months, would have a lung cancer excess lifetime risk of about 35 to 40 lung cancer deaths per 1000 exposed miners. Modern mining methods using dilution ventilation as well as bulkheading and backfilling techniques make it possible to achieve substantial reductions in the cumulative exposure to radon progeny. Information was provided on sampling strategy, control technology, ventilation systems, respirators, and medical surveillance programs

A study on PubMed search tag usage pattern: association rule mining of a full-day PubMed query log.

Science.gov (United States)

Mosa, Abu Saleh Mohammad; Yoo, Illhoi

2013-01-09

The practice of evidence-based medicine requires efficient biomedical literature search such as PubMed/MEDLINE. Retrieval performance relies highly on the efficient use of search field tags. The purpose of this study was to analyze PubMed log data in order to understand the usage pattern of search tags by the end user in PubMed/MEDLINE search. A PubMed query log file was obtained from the National Library of Medicine containing anonymous user identification, timestamp, and query text. Inconsistent records were removed from the dataset and the search tags were extracted from the query texts. A total of 2,917,159 queries were selected for this study issued by a total of 613,061 users. The analysis of frequent co-occurrences and usage patterns of the search tags was conducted using an association mining algorithm. The percentage of search tag usage was low (11.38% of the total queries) and only 2.95% of queries contained two or more tags. Three out of four users used no search tag and about two-third of them issued less than four queries. Among the queries containing at least one tagged search term, the average number of search tags was almost half of the number of total search terms. Navigational search tags are more frequently used than informational search tags. While no strong association was observed between informational and navigational tags, six (out of 19) informational tags and six (out of 29) navigational tags showed strong associations in PubMed searches. The low percentage of search tag usage implies that PubMed/MEDLINE users do not utilize the features of PubMed/MEDLINE widely or they are not aware of such features or solely depend on the high recall focused query translation by the PubMed's Automatic Term Mapping. The users need further education and interactive search application for effective use of the search tags in order to fulfill their biomedical information needs from PubMed/MEDLINE.

9 CFR 201.4 - Bylaws, rules and regulations, and requirements of exchanges, associations, or other...

Science.gov (United States)

2010-01-01

... 9 Animals and Animal Products 2 2010-01-01 2010-01-01 false Bylaws, rules and regulations, and... 201.4 Animals and Animal Products GRAIN INSPECTION, PACKERS AND STOCKYARDS ADMINISTRATION (PACKERS AND... of any exchange, association, or other organization, or any other valid law, rule or regulation, or...

Impact of gold mining associated with mercury contamination in soil, biota sediments and tailings in Kenya.

Science.gov (United States)

Odumo, Benjamin Okang'; Carbonell, Gregoria; Angeyo, Hudson Kalambuka; Patel, Jayanti Purshottam; Torrijos, Manuel; Rodríguez Martín, José Antonio

2014-11-01

This work considered the environmental impact of artisanal mining gold activity in the Migori-Transmara area (Kenya). From artisanal gold mining, mercury is released to the environment, thus contributing to degradation of soil and water bodies. High mercury contents have been quantified in soil (140 μg kg(-1)), sediment (430 μg kg(-1)) and tailings (8,900 μg kg(-1)), as expected. The results reveal that the mechanism for transporting mercury to the terrestrial ecosystem is associated with wet and dry depositions. Lichens and mosses, used as bioindicators of pollution, are related to the proximity to mining areas. The further the distance from mining areas, the lower the mercury levels. This study also provides risk maps to evaluate potential negative repercussions. We conclude that the Migori-Transmara region can be considered a strongly polluted area with high mercury contents. The technology used to extract gold throughout amalgamation processes causes a high degree of mercury pollution around this gold mining area. Thus, alternative gold extraction methods should be considered to reduce mercury levels that can be released to the environment.

Arsenic partitioning among particle-size fractions of mine wastes and stream sediments from cinnabar mining districts.

Science.gov (United States)

Silva, Veronica; Loredo, Jorge; Fernández-Martínez, Rodolfo; Larios, Raquel; Ordóñez, Almudena; Gómez, Belén; Rucandio, Isabel

2014-10-01

Tailings from abandoned mercury mines represent an important pollution source by metals and metalloids. Mercury mining in Asturias (north-western Spain) has been carried out since Roman times until the 1970s. Specific and non-specific arsenic minerals are present in the paragenesis of the Hg ore deposit. As a result of intensive mining operations, waste materials contain high concentrations of As, which can be geochemically dispersed throughout surrounding areas. Arsenic accumulation, mobility and availability in soils and sediments are strongly affected by the association of As with solid phases and granular size composition. The objective of this study was to examine phase associations of As in the fine grain size subsamples of mine wastes (La Soterraña mine site) and stream sediments heavily affected by acid mine drainage (Los Rueldos mine site). An arsenic-selective sequential procedure, which categorizes As content into seven phase associations, was applied. In spite of a higher As accumulation in the finest particle-size subsamples, As fractionation did not seem to depend on grain size since similar distribution profiles were obtained for the studied granulometric fractions. The presence of As was relatively low in the most mobile forms in both sites. As was predominantly linked to short-range ordered Fe oxyhydroxides, coprecipitated with Fe and partially with Al oxyhydroxides and associated with structural material in mine waste samples. As incorporated into short-range ordered Fe oxyhydroxides was the predominant fraction at sediment samples, representing more than 80% of total As.

A review of the environmental management practices in the Indian mining sector

Energy Technology Data Exchange (ETDEWEB)

Banerjee, S.P.

2005-06-01

The article, by the former director of the Indian school of Mines, Dhanbad, covers the major environmental management issues common to the majority of India's mining projects and gives recommendations for further improving practices, such as mine planning, fly ash utilization and conversion of mined lands to agriculture. India is likely to expand activities in mining of coal, iron ores, limestone and bauxite, mostly through opencast mining. The article mentions the regulations controlling environmental impacts of mining, the 1986 Environmental Protection Act and other measures. The 1994 Environmental Impact Assessment process was modified in 1997 to include the concept of public hearing. In 2003 the requirement of public hearing was limited to mines with lease area above 25 ha. In 2003 the Mineral Conservation and Development rules were amended to improve protection of the environment, reclamation and rehabilitation measures. Further deforestation cannot be avoided but the industry record for creating new forests on mined areas is improving. Issues of waste water treatment and dust abatement are outlined. 8 refs.

Mitigation of social and environmental impacts resulting from final closure of uranium mines

International Nuclear Information System (INIS)

Cipriani, Moacir

2002-11-01

This thesis focus on the impact of uranium mines in Brazil. It is recent, in the order of the Brazilian mining, the concern with the impact of mining activities. The Federal Constitution of 1988 compels the miner to rehabilitate the degraded environment, in accordance with the technical solution demanded by the competent public agency, which makes use of a system of environmental norms conditioning the mining activity. However, the concern with the closure of mines is in an early stage, for whose achievement the public power still lacks of norms and regulations. The closure of the first uranium mining in Brazil assumes special meaning, because the possible environmental problems related to uranium mines are considered to be serious and the uranium industry is state owned. This thesis is divided in two sections. The first one describes the state of the art of the uranium industry and the rules and management practices regarding the final closure of uranium mining in Brazil and countries like Australia, Canada, USA and France, that have been selected on the basis of the following criteria: production, exportation, control of reserves and final consumption of uranium. In the second part, a case study of Pocos de Caldas mine is presented, with description of historical production, plant waste and the chemical treatment of the ore. This part also presents the research carried out since the beginning of the operations aiming to remedial actions, including the dismantling of surface structures, tailings reclamation, and ground-water restoration, following CNEN (Brazilian Nuclear Energy Commission) rules, as well as a survey of local press coverage of the impact of the industry. A final recommendation is made regarding a management model and strategies to mitigate social and environmental impacts resulting from final closure of the CIPC. (author)

Neutralization and attenuation of metal species in acid mine drainage and mine leachates using magnesite: a batch experimental approach

CSIR Research Space (South Africa)

Masindi, Vhahangwele

2014-08-01

Full Text Available International Mine Water Association Conference – An Interdisciplinary Response to Mine Water Challenges, China University of Mining and Technogy, China, China, 18-22 August 2014 Neutralization and Attenuation of Metal Species in Acid Mine Drainage and Mine...

Knowledge discovery with classification rules in a cardiovascular dataset.

Science.gov (United States)

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.

Associations between parental rules, style of communication and children's screen time.

Science.gov (United States)

Bjelland, Mona; Soenens, Bart; Bere, Elling; Kovács, Éva; Lien, Nanna; Maes, Lea; Manios, Yannis; Moschonis, George; te Velde, Saskia J

2015-10-01

Research suggests an inverse association between parental rules and screen time in pre-adolescents, and that parents' style of communication with their children is related to the children's time spent watching TV. The aims of this study were to examine associations of parental rules and parental style of communication with children's screen time and perceived excessive screen time in five European countries. UP4FUN was a multi-centre, cluster randomised controlled trial with pre- and post-test measurements in each of five countries; Belgium, Germany, Greece, Hungary and Norway. Questionnaires were completed by the children at school and the parent questionnaire was brought home. Three structural equation models were tested based on measures of screen time and parental style of communication from the pre-test questionnaires. Of the 152 schools invited, 62 (41 %) schools agreed to participate. In total 3325 children (average age 11.2 years and 51 % girls) and 3038 parents (81 % mothers) completed the pre-test questionnaire. The average TV/DVD times across the countries were between 1.5 and 1.8 h/day, while less time was used for computer/games console (0.9-1.4 h/day). The children's perceived parental style of communication was quite consistent for TV/DVD and computer/games console. The presence of rules was significantly associated with less time watching TV/DVD and use of computer/games console time. Moreover, the use of an autonomy-supportive style was negatively related to both time watching TV/DVD and use of computer/games console time. The use of a controlling style was related positively to perceived excessive time used on TV/DVD and excessive time used on computer/games console. With a few exceptions, results were similar across the five countries. This study suggests that an autonomy-supportive style of communicating rules for TV/DVD or computer/ games console use is negatively related to children's time watching TV/DVD and use of computer/games console time

Cost efficiency of the non-associative flow rule simulation of an industrial component

Science.gov (United States)

Galdos, Lander; de Argandoña, Eneko Saenz; Mendiguren, Joseba

2017-10-01

In the last decade, metal forming industry is becoming more and more competitive. In this context, the FEM modeling has become a primary tool of information for the component and process design. Numerous researchers have been focused on improving the accuracy of the material models implemented on the FEM in order to improve the efficiency of the simulations. Aimed at increasing the efficiency of the anisotropic behavior modelling, in the last years the use of non-associative flow rule models (NAFR) has been presented as an alternative to the classic associative flow rule models (AFR). In this work, the cost efficiency of the used flow rule model has been numerically analyzed by simulating an industrial drawing operation with two different models of the same degree of flexibility: one AFR model and one NAFR model. From the present study, it has been concluded that the flow rule has a negligible influence on the final drawing prediction; this is mainly driven by the model parameter identification procedure. Even though the NAFR formulation is complex when compared to the AFR, the present study shows that the total simulation time while using explicit FE solvers has been reduced without loss of accuracy. Furthermore, NAFR formulations have an advantage over AFR formulations in parameter identification because the formulation decouples the yield stress and the Lankford coefficients.

The South African mining industry

International Nuclear Information System (INIS)

Langton, G.

1982-01-01

This paper covers six of the many mining and associated developments in South Africa. These are: (1) Deep level gold mining at Western Deep Levels Limited - (2) Palabora Mining Company Limited - SA's unique copper mine - (3) Production of steel and vanadium-rich slag at Highveld Steel and Vanadium Corporation - (4) Coal mining at Kriel and Kleinkopje Collieries - (5) A mass mining system for use below the Gabbro Sill at Premier Diamond Mine - (6) Uranium production - joint metallurgical scheme- Orange Free State Gold Mines. - For publication in this journal the original paper has been summarised. Should any reader wish to have the full text in English he should write to the author at the address below. (orig.) [de

Improved Personalized Recommendation Based on Causal Association Rule and Collaborative Filtering

Science.gov (United States)

Lei, Wu; Qing, Fang; Zhou, Jin

2016-01-01

There are usually limited user evaluation of resources on a recommender system, which caused an extremely sparse user rating matrix, and this greatly reduce the accuracy of personalized recommendation, especially for new users or new items. This paper presents a recommendation method based on rating prediction using causal association rules.…

Perancangan Dan Pembuatan Modul Data Mining Market Basket Analysis Pada Odoo Dengan Studi Kasus Supermarket X

OpenAIRE

Hendratha, Stefani Natalia; Yulia, Yulia; Budhi, Gregorius Satia

2016-01-01

Odoo Enterprise Resource Planning (ERP) system storing company's transaction data. However, Odoo doesn't have a module for managing data. It takes a module for managing data into useful information.Based on the above problems, a module for data mining Market Basket Analysis is being designed. This module uses FP-Growth algorithm by utilizing the sales transaction data.For the testing, this module using data from X Supermarket. The final result of this module is an association rule from data m...

Human mercury exposure associated with small-scale gold mining in Burkina Faso.

Science.gov (United States)

Tomicic, Catherine; Vernez, David; Belem, Tounaba; Berode, Michèle

2011-06-01

In Burkina Faso, gold ore is one of the main sources of income for an important part of the active population. Artisan gold miners use mercury in the extraction, a toxic metal whose human health risks are well known. The aim of the present study was to assess mercury exposure as well as to understand the exposure determinants of gold miners in Burkinabe small-scale mines. The examined gold miners' population on the different selected gold mining sites was composed by persons who were directly and indirectly related to gold mining activities. But measurement of urinary mercury was performed on workers most susceptible to be exposed to mercury. Thus, occupational exposure to mercury was evaluated among ninety-three workers belonging to eight different gold mining sites spread in six regions of Burkina Faso. Among others, work-related exposure determinants were taken into account for each person during urine sampling as for example amalgamating or heating mercury. All participants were medically examined by a local medical team in order to identify possible symptoms related to the toxic effect of mercury. Mercury levels were high, showing that 69% of the measurements exceeded the ACGIH (American Conference of Industrial Hygienists) biological exposure indice (BEI) of 35 μg per g of creatinine (μg/g-Cr) (prior to shift) while 16% even exceeded 350 μg/g-Cr. Basically, unspecific but also specific symptoms related to mercury toxicity could be underlined among the persons who were directly related to gold mining activities. Only one-third among the studied subpopulation reported about less than three symptoms possibly associated to mercury exposure and nearly half of them suffered from at least five of these symptoms. Ore washers were more involved in the direct handling of mercury while gold dealers in the final gold recovery activities. These differences may explain the overexposure observed in gold dealers and indicate that the refining process is the major source

APLIKASI DATA MINING MARKET BASKET ANALYSIS PADA TABEL DATA ABSENSI ELEKTRONIK UNTUK MENDETEKSI KECURANGAN ABSENSI (CHECK-LOCK KARYAWAN DI PERUSAHAAN

Directory of Open Access Journals (Sweden)

Gregorius Satia Budhi

2007-01-01

Full Text Available Taking attendance from employees always becomes a problem for Human Resource Department (HRD in many companies lately. Although there is an automatic check-lock machine, it still has a weakness. This machine can't detect some frauds like the employee swipes double identity card, his card and the others card. Reseachers want to solve this problem by using data mining method, especially market basket analysis.This software will transform the attendance data to compact transaction format by using MaxDiff Histogram method. And it will be processed into frequent itemset with Pincer Search Algoritm. At the final process the employee's association rule will got from frequent itemset. This output will be served to user that is the HRD of a firm.Testing result shows that Data Mining Market Basket Analysis can be used to get pattern of employee's check-lock from a company. And this pattern can help user to detect fraud that is done by employee. Abstract in Bahasa Indonesia : Absensi pegawai selama ini selalu menjadi permasalahan yang pelik bagi bagian HRD di perusahaan - perusahaan yang ada. Walaupun telah ada peralatan absensi otomatis, alat ini masih memiliki kelemahan yaitu, tidak dapat mendeteksi kecurangan pegawai untuk menitipkan kartu absensinya pada karyawan lain untuk diabsenkan. Peneliti berkeinginan untuk mengatasi permasalahan absensi tersebut dengan memanfaatkan metode data mining, khususnya metode market basket analysis, untuk mendeteksi kecurangan ini.Perangkat lunak yang dibuat ini akan mentranformasikan data absensi pegawai menggunakan metode MaxDiff Histogram menjadi format compact transaction yang selanjutnya akan diproses menggunakan Algoritma Pincer Search menjadi frequent itemset. Pada akhirnya dari data frequent itemset ini didapat association rule pegawai untuk disajikan kepada pengguna, yaitu bagian HRD perusahaan.Dari hasil pengujian dapat diketahui bahwa metode Data Mining Market Basket Analysis dapat dimanfaatkan untuk menggali

The use of Data Mining techniques to Automated Detection of Beneficiaries With Indicative of Diabetes Mellitus 2

Directory of Open Access Journals (Sweden)

Deborah Ribeiro Carvalho

2015-09-01

Full Text Available Introduction: The Health Industry companies store a vast amount of data in order to support administrative tasks like payment of medical bills, but filling out epidemiological data (International Classification of Diseases - ICD is not mandatory. This makes it difficult to identify the persons’ illness using standard data extraction techniques as well as implementing preventive programs. Objective: This paper proposes a data mining model that identifies automatically the patients with chronic illnesses. Method: The proposed method is comprised of the following steps: initial identification of the variables and their analysis; variable selection; data mining and rule validation by experts. An experiment, for identifying the patients with propensity for diabetes type 2, was designed to validate the methodology. Results: For the data mining process, 12 variables were selected, targeting 43.375 patients: 843 rules were discovered, with a 88,9% success rate. Conclusion: From the 843 rules, six were selected to be evaluated by four experts: they considered the model efficient, with an 89.6% rate of positive results.

Ecological and human health risks associated with abandoned gold mine tailings contaminated soil.

Directory of Open Access Journals (Sweden)

Veronica Mpode Ngole-Jeme

Full Text Available Gold mining is a major source of metal and metalloid emissions into the environment. Studies were carried out in Krugersdorp, South Africa, to evaluate the ecological and human health risks associated with exposure to metals and metalloids in mine tailings contaminated soils. Concentrations of arsenic (As, cadmium (Cd, chromium (Cr, cobalt (Co, copper (Cu, lead (Pb, manganese (Mn, nickel (Ni, and zinc (Zn in soil samples from the area varied with the highest contamination factors (expressed as ratio of metal or metalloid concentration in the tailings contaminated soil to that of the control site observed for As (3.5x102, Co (2.8x102 and Ni (1.1x102. Potential ecological risk index values for metals and metalloids determined from soil metal and metalloid concentrations and their respective risk factors were correspondingly highest for As (3.5x103 and Co (1.4x103, whereas Mn (0.6 presented the lowest ecological risk. Human health risk was assessed using Hazard Quotient (HQ, Chronic Hazard Index (CHI and carcinogenic risk levels, where values of HQ > 1, CHI > 1 and carcinogenic risk values > 1×10-4 represent elevated risks. Values for HQ indicated high exposure-related risk for As (53.7, Cr (14.8, Ni (2.2, Zn (2.64 and Mn (1.67. Children were more at risk from heavy metal and metalloid exposure than adults. Cancer-related risks associated with metal and metalloid exposure among children were also higher than in adults with cancer risk values of 3×10-2 and 4×10-2 for As and Ni respectively among children, and 5×10-3 and 4×10-3 for As and Ni respectively among adults. There is significant potential ecological and human health risk associated with metal and metalloid exposure from contaminated soils around gold mine tailings dumps. This could be a potential contributing factor to a setback in the health of residents in informal settlements dominating this mining area as the immune systems of some of these residents are already compromised by high

RENEGOTIATION MINING CONTRACT: LEGAL PARADIGM RECONSTRUCTION EFFORTS

Directory of Open Access Journals (Sweden)

Marilang -

2014-07-01

Full Text Available Renegotiation contract mining is not a priori notion that was born but is driven by the fact that empirical Work Contract (KK and coal mining concessions of the Works Agreement (Cca that are valid for this resulted in profits which are not comparable between countries with investors (domestic and foreign. In addition, Law No. 4 of 2009 about Mineral and Coal Mining (minerba through article 169 have been injected that though the mining contracts during the validity of this, still respected until the end, however, if the implementation of these contracts give rise to distortions for the national interest, then the Government must encourage the investors to do Renegotiation against existing contracts to comply with legislation minerba forever within a period of one year since the enactment of the legislation this minerba. Renegotiation mining contracts that have been approved on the fact of the matter is simply an attempt to reconstruct the ruling paradigm, so with that paradigm shift, both parties can reach the intersection for the benefit of both parties, i.e. the parties proportionately Indonesia suffered no losses on the one hand, and the benefit of the domestic and foreign investors remain in reasonable limits on the other. 

Integrated Text Mining and Chemoinformatics Analysis Associates Diet to Health Benefit at Molecular Level

DEFF Research Database (Denmark)

Jensen, Kasper; Panagiotou, Gianni; Kouskoumvekaki, Irene

2014-01-01

, lipids and nutrients. In this work, we applied text mining and Naïve Bayes classification to assemble the knowledge space of food-phytochemical and food-disease associations, where we distinguish between disease prevention/amelioration and disease progression. We subsequently searched for frequently...

Alkemio: association of chemicals with biomedical topics by text and data mining.

Science.gov (United States)

Gijón-Correas, José A; Andrade-Navarro, Miguel A; Fontaine, Jean F

2014-07-01

The PubMed® database of biomedical citations allows the retrieval of scientific articles studying the function of chemicals in biology and medicine. Mining millions of available citations to search reported associations between chemicals and topics of interest would require substantial human time. We have implemented the Alkemio text mining web tool and SOAP web service to help in this task. The tool uses biomedical articles discussing chemicals (including drugs), predicts their relatedness to the query topic with a naïve Bayesian classifier and ranks all chemicals by P-values computed from random simulations. Benchmarks on seven human pathways showed good retrieval performance (areas under the receiver operating characteristic curves ranged from 73.6 to 94.5%). Comparison with existing tools to retrieve chemicals associated to eight diseases showed the higher precision and recall of Alkemio when considering the top 10 candidate chemicals. Alkemio is a high performing web tool ranking chemicals for any biomedical topics and it is free to non-commercial users. http://cbdm.mdc-berlin.de/∼medlineranker/cms/alkemio. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Examining health and well-being outcomes associated with mining activity in rural communities of high-income countries: A systematic review.

Science.gov (United States)

Mactaggart, Fiona; McDermott, Liane; Tynan, Anna; Gericke, Christian

2016-08-01

It is recognised internationally that rural communities often experience greater barriers to accessing services and have poorer health outcomes compared to urban communities. In some settings, health disparities may be further exacerbated by mining activity, which can affect the social, physical and economic environment in which rural communities reside. Direct environmental health impacts are often associated with mining activity and are frequently investigated. However, there is evidence of broader, indirect health and well-being implications emerging in the literature. This systematic review examines these health and well-being outcomes in communities living in proximity to mining in high-income countries, and, in doing so, discusses their possible determinants. Four databases were systematically searched. Articles were selected if adult residents in mining communities were studied and outcomes were related to health or individual or community-level well-being. A narrative synthesis was conducted. Sixteen publications were included. Evidence of increased prevalence of chronic diseases and poor self-reported health status was reported in the mining communities. Relationship breakdown and poor family health, lack of social connectedness and decreased access to health services were also reported. Changes to the physical landscape; risky health behaviours; shift work of partners in the mine industry; social isolation and cyclical nature of 'boom and bust' activity contributed to poorer outcomes in the communities. This review highlights the broader health and well-being outcomes associated with mining activity that should be monitored and addressed in addition to environmental health impacts to support co-existence of mining activities and rural communities. © 2016 National Rural Health Alliance Inc.

Mining TCGA data using Boolean implications.

Directory of Open Access Journals (Sweden)

Subarna Sinha

Full Text Available 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/.

Radioecological situation in the area of the Priargun Production Mining and Chemical Association in Russian Zabaikalye Territory

International Nuclear Information System (INIS)

Shandala, N.K.; Titov, A.V.; Kiselev, S.M.; Akhromeev, S.V.; Semenova, M.P.

2012-01-01

'The Priargun Production Mining and Chemical Association' (hereinafter referred to as PPMCA) is a diversified mining company, which, in addition to underground mining of uranium ore, carries out refining of such ores in hydrometallurgical process to produce natural uranium oxide. The PPMCA facilities are sources of radiation and chemical contamination of the environment in the areas of their location. Contamination of local parts of the health protection zone results from discharges and releases, dust transport of radionuclides from the mine rock dumps containing uranium, radium, radon and its decay products, spillage of ore along highways, straits uraniferous pulp and mine water from the pool pump shaft station. The radionuclide migration results in groundwater contamination. In order to establish the strategy and develop criteria for the site remediation, independent radiation hygienic monitoring is being carried out over some years. The subjects of research include: soil, grass and media of open ponds (water, bottom sediments, water vegetation). We also measured the radon activity concentration inside surface workshops and auxiliaries. We determined the specific activity of natural radionuclides. The researches performed showed that in soil, vegetation, groundwater and local foods sampled in the vicinity of the uranium mines, there is a significant excess of 226 Ra and 2 32 Th content compared to areas outside the zone of influence of uranium mining.

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

Science.gov (United States)

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

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

Directory of Open Access Journals (Sweden)

Matej Mihelčić

Full Text Available 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

Chronic respiratory disease among the elderly in South Africa: any association with proximity to mine dumps?

Science.gov (United States)

Nkosi, Vusumuzi; Wichmann, Janine; Voyi, Kuku

2015-04-03

There is increasing evidence that environmental factors such as air pollution from mine dumps, increase the risk of chronic respiratory symptoms and diseases. The aim of this study was to investigate the association between proximity to mine dumps and prevalence of chronic respiratory disease in people aged 55 years and older. Elderly persons in communities 1-2 km (exposed) and 5 km (unexposed), from five pre-selected mine dumps in Gauteng and North West Province, in South Africa were included in a cross-sectional study. Structured interviews were conducted with 2397 elderly people, using a previously validated ATS-DLD-78 questionnaire from the British Medical Research Council. Exposed elderly persons had a significantly higher prevalence of chronic respiratory symptoms and diseases than those who were unexposed., Results from the multiple logistic regression analysis indicated that living close to mine dumps was significantly associated with asthma (OR = 1.57; 95% CI: 1.20 - 2.05), chronic bronchitis (OR = 1.74; 95 CI: 1.25 - 2.39), chronic cough (OR = 2.02; 95% CI: 1.58 - 2.57), emphysema (OR = 1.75; 95% CI: 1.11 - 2.77), pneumonia (OR = 1.38; 95% CI: 1.07 - 1.77) and wheeze (OR = 2.01; 95% CI: 1.73 - 2.54). Residing in exposed communities, current smoking, ex-smoking, use of paraffin as main residential cooking/heating fuel and low level of education emerged as independent significant risk factors for chronic respiratory symptoms and diseases. This study suggests that there is a high level of chronic respiratory symptoms and diseases among elderly people in communities located near to mine dumps in South Africa.

Mineralogic and element association of coals from the Gevra mine, Korba coal field, Madhya Pradesh, India

International Nuclear Information System (INIS)

Hart, B.R.; Powell, M.A.; Fyfe, W.S.; Sahu, K.C.; Tripathy, S.

1991-01-01

As a part of a project to study the content and distribution of trace elements in coals and coal by-products from coal mining areas of India, the mineral and elemental composition of the whole coal and concentration of the selective elements in the whole coal mined from the Gevra mine of the Korba coal field in Madhya Pradesh are studied. The vertical trend of the selected elements are defined and possible relationships of these elements to the minerals present in or associated with the above coal are examined. The Gevra Coals have ash contact ( 3 times world average) and low sulphur content (1/4 of world average). Most elements are found to be positively correlated with ash indicating an inorganic association. Elements which show organic affinity include S, Cl, I and In. Fe, Cu, Zn and Pb occur in sulfide phases, the dominant Fe phase identified is siderite which also contain Mn and Mg. Th and U along with varying proportions of rare earths, Ce, Dy, La and Y have been identified with discrete phosphate minerals. Most trace elements have been found to be concentrated in the upper and lower portions of the coal seam exposed in Gevra mine and in partings. It is, therefore, suggested that selective mining and removal of high ash/inorganic material, particularly the upper and lower portion of the seam, will greatly reduce the mobilization of Al, As, Co, Fe, Hf, Sc, Si, Ti and to a lesser degree Cr, La, Mn, Th U and V during combustion of coal in power plants and consequently will reduce the influx of trace elements to the environment. (M.G.B.). 23 refs., 5 figs

Radiation in mines

International Nuclear Information System (INIS)

Rose, H.J.M.

1982-01-01

Radiation in mines is primarily associated with, but not restricted to, the exploitation of uranium bearing orebodies. The intent of this chapter is to convey some aspects of radiation control in the mining industry, the behaviour of the parent radon and its daughter products. An attempt was made to demonstrate that anything less than complete diligence by the ventilation personnel could result in rapid deterioration of the mine environment, and consequently high exposure rates. When the maximum annual exposure limit is 4,0 WLM (Working level month exposure) the ventilation official is not allowed the privilege of making an error. Ventilation planning in uranium mines is of prime importance and is very much a group involvement

Data Mining Rules for Ultrasonic B-Type Detection and Diagnosis for Cholecystolithiasis

Institute of Scientific and Technical Information of China (English)

LOU Wei; YAN Li-min; HE Guo-sen

2004-01-01

This paper presents realistic data mining based on the data of B-type ultrasonic detection and diagnosis for cholrcystolithiasis (gallbladder stone in biliary tract) recorded by a district central hospital in Shanghai during the past several years. Computer simulation and modeling is described.

Understanding the mobilisation of metal pollution associated with historical mining in a carboniferous upland catchment.

Science.gov (United States)

Valencia-Avellan, Magaly; Slack, Rebecca; Stockdale, Anthony; Mortimer, Robert John George

2017-08-16

Point and diffuse pollution from metal mining has led to severe environmental damage worldwide. Mine drainage is a significant problem for riverine ecosystems, it is commonly acidic (AMD), but neutral mine drainage (NMD) can also occur. A representative environment for studying metal pollution from NMD is provided by carboniferous catchments characterised by a circumneutral pH and high concentrations of carbonates, supporting the formation of secondary metal-minerals as potential sinks of metals. The present study focuses on understanding the mobility of metal pollution associated with historical mining in a carboniferous upland catchment. In the uplands of the UK, river water, sediments and spoil wastes were collected over a period of fourteen months, samples were chemically analysed to identify the main metal sources and their relationships with geological and hydrological factors. Correlation tests and principal component analysis suggest that the underlying limestone bedrock controls pH and weathering reactions. Significant metal concentrations from mining activities were measured for zinc (4.3 mg l -1 ), and lead (0.3 mg l -1 ), attributed to processes such as oxidation of mined ores (e.g. sphalerite, galena) or dissolution of precipitated secondary metal-minerals (e.g. cerussite, smithsonite). Zinc and lead mobility indicated strong dependence on biogeochemistry and hydrological conditions (e.g. pH and flow) at specific locations in the catchment. Annual loads of zinc and lead (2.9 and 0.2 tonnes per year) demonstrate a significant source of both metals to downstream river reaches. Metal pollution results in a large area of catchment having a depleted chemical status with likely effects on the aquatic ecology. This study provides an improved understanding of geological and hydrological processes controlling water chemistry, which is critical to assessing metal sources and mobilization, especially in neutral mine drainage areas.

Uranium mining

International Nuclear Information System (INIS)

2008-01-01

Full text: The economic and environmental sustainability of uranium mining has been analysed by Monash University researcher Dr Gavin Mudd in a paper that challenges the perception that uranium mining is an 'infinite quality source' that provides solutions to the world's demand for energy. Dr Mudd says information on the uranium industry touted by politicians and mining companies is not necessarily inaccurate, but it does not tell the whole story, being often just an average snapshot of the costs of uranium mining today without reflecting the escalating costs associated with the process in years to come. 'From a sustainability perspective, it is critical to evaluate accurately the true lifecycle costs of all forms of electricity production, especially with respect to greenhouse emissions, ' he says. 'For nuclear power, a significant proportion of greenhouse emissions are derived from the fuel supply, including uranium mining, milling, enrichment and fuel manufacture.' Dr Mudd found that financial and environmental costs escalate dramatically as the uranium ore is used. The deeper the mining process required to extract the ore, the higher the cost for mining companies, the greater the impact on the environment and the more resources needed to obtain the product. I t is clear that there is a strong sensitivity of energy and water consumption and greenhouse emissions to ore grade, and that ore grades are likely to continue to decline gradually in the medium to long term. These issues are critical to the current debate over nuclear power and greenhouse emissions, especially with respect to ascribing sustainability to such activities as uranium mining and milling. For example, mining at Roxby Downs is responsible for the emission of over one million tonnes of greenhouse gases per year and this could increase to four million tonnes if the mine is expanded.'

Study on Association between Spatial Distribution of Metal Mines and Disease Mortality: A Case Study in Suxian District, South China

Science.gov (United States)

Song, Daping; Jiang, Dong; Wang, Yong; Chen, Wei; Huang, Yaohuan; Zhuang, Dafang

2013-01-01

Metal mines release toxic substances into the environment and can therefore negatively impact the health of residents in nearby regions. This paper sought to investigate whether there was excess disease mortality in populations in the vicinity of the mining area in Suxian District, South China. The spatial distribution of metal mining and related activities from 1985 to 2012, which was derived from remote sensing imagery, was overlapped with disease mortality data. Three hotspot areas with high disease mortality were identified around the Shizhuyuan mine sites, i.e., the Dengjiatang metal smelting sites, and the Xianxichong mine sites. Disease mortality decreased with the distance to the mining and smelting areas. Population exposure to pollution was estimated on the basis of distance from town of residence to pollution source. The risk of dying according to disease mortality rates was analyzed within 7–25 km buffers. The results suggested that there was a close relationship between the risk of disease mortality and proximity to the Suxian District mining industries. These associations were dependent on the type and scale of mining activities, the area influenced by mining and so on. PMID:24135822

Ontology-based literature mining of E. coli vaccine-associated gene interaction networks.

Science.gov (United States)

Hur, Junguk; Özgür, Arzucan; He, Yongqun

2017-03-14

Pathogenic Escherichia coli infections cause various diseases in humans and many animal species. However, with extensive E. coli vaccine research, we are still unable to fully protect ourselves against E. coli infections. To more rational development of effective and safe E. coli vaccine, it is important to better understand E. coli vaccine-associated gene interaction networks. In this study, we first extended the Vaccine Ontology (VO) to semantically represent various E. coli vaccines and genes used in the vaccine development. We also normalized E. coli gene names compiled from the annotations of various E. coli strains using a pan-genome-based annotation strategy. The Interaction Network Ontology (INO) includes a hierarchy of various interaction-related keywords useful for literature mining. Using VO, INO, and normalized E. coli gene names, we applied an ontology-based SciMiner literature mining strategy to mine all PubMed abstracts and retrieve E. coli vaccine-associated E. coli gene interactions. Four centrality metrics (i.e., degree, eigenvector, closeness, and betweenness) were calculated for identifying highly ranked genes and interaction types. Using vaccine-related PubMed abstracts, our study identified 11,350 sentences that contain 88 unique INO interactions types and 1,781 unique E. coli genes. Each sentence contained at least one interaction type and two unique E. coli genes. An E. coli gene interaction network of genes and INO interaction types was created. From this big network, a sub-network consisting of 5 E. coli vaccine genes, including carA, carB, fimH, fepA, and vat, and 62 other E. coli genes, and 25 INO interaction types was identified. While many interaction types represent direct interactions between two indicated genes, our study has also shown that many of these retrieved interaction types are indirect in that the two genes participated in the specified interaction process in a required but indirect process. Our centrality analysis of

Fuelcell-Hybrid Mine loader (LHD)

Energy Technology Data Exchange (ETDEWEB)

James L Dippo; Tim Erikson; Kris Hess

2009-07-10

The fuel cell hybrid mine loader project, sponsored by a government-industry consortium, was implemented to determine the viability of proton exchange membrane (PEM) fuel cells in underground mining applications. The Department of Energy (DOE) sponsored this project with cost-share support from industry. The project had three main goals: (1) to develop a mine loader powered by a fuel cell, (2) to develop associated metal-hydride storage and refueling systems, and (3) to demonstrate the fuel cell hybrid loader in an underground mine in Nevada. The investigation of a zero-emissions fuel cell power plant, the safe storage of hydrogen, worker health advantages (over the negative health effects associated with exposure to diesel emissions), and lower operating costs are all key objectives for this project.

Uranium mining and heap leaching in India and related safety measures - A case study of Jajawal mines

International Nuclear Information System (INIS)

Saxena, V.P.; Verma, S.C.

2001-01-01

Exploration and exploitation of uranium involves drilling, mining, milling and extraction processes including heap leaching in some cases. At the exploration stage, the country's laws related to statutory environmental clearance covering forest and sanctuaries or Coastal Regulatory Zones (CRZ) are equally applicable for atomic minerals. At the developmental mining or commercial exploitation stage in addition to the environmental impact assessment, the provisions of Atomic Energy (working of Mines, Minerals and handling of Prescribed Substances) Rules 1984 are also to be followed which covers radiation monitoring, pollution control and other safety measures which are enforced by licensing authorities and the Atomic Energy Regulatory Board (AERB) of India. In India, Jaduguda, Bhatin, Narwapahar in Singhbhum Thrust Belt (STB), Asthota and Khiya in Siwaliks, Domiasiat in Cretaceous sandstones, Bodal and Jajawal in Precambrian crystallines, are some of the centres where mining has been carried out up to various underground levels. Substantial amount of dust and radon gas are generated during mining and milling operations. Though uranium mining is considered as hazardous for contamination by radionuclides, it is observed that many non-uranium mines have registered up to 100 mWL radon concentration, e.g. copper mines in STB area show up to 900 mewl in a few cases. Compared to this the Uranium mines in India have not shown any increase over the limits prescribed by AERB. Specific problems associated with mining include release of radon and other radioactive pollutants like Th-230, Ra-226, Pb-210 and Po-210, substantial dust generation, ground water contamination, proximity of population to working mines and environmental surveillance. These problems are adequately handled by periodical monitoring of various radiological parameters such as radon daughter working level, long lived alpha activity and concentration of radionuclides in gaseous, liquid and solid medium. Pre

Data mining in radiology

International Nuclear Information System (INIS)

Kharat, Amit T; Singh, Amarjit; Kulkarni, Vilas M; Shah, Digish

2014-01-01

Data mining facilitates the study of radiology data in various dimensions. It converts large patient image and text datasets into useful information that helps in improving patient care and provides informative reports. Data mining technology analyzes data within the Radiology Information System and Hospital Information System using specialized software which assesses relationships and agreement in available information. By using similar data analysis tools, radiologists can make informed decisions and predict the future outcome of a particular imaging finding. Data, information and knowledge are the components of data mining. Classes, Clusters, Associations, Sequential patterns, Classification, Prediction and Decision tree are the various types of data mining. Data mining has the potential to make delivery of health care affordable and ensure that the best imaging practices are followed. It is a tool for academic research. Data mining is considered to be ethically neutral, however concerns regarding privacy and legality exists which need to be addressed to ensure success of data mining

DDMGD: the database of text-mined associations between genes methylated in diseases from different species

KAUST Repository

Raies, A. B.

2014-11-14

Gathering information about associations between methylated genes and diseases is important for diseases diagnosis and treatment decisions. Recent advancements in epigenetics research allow for large-scale discoveries of associations of genes methylated in diseases in different species. Searching manually for such information is not easy, as it is scattered across a large number of electronic publications and repositories. Therefore, we developed DDMGD database (http://www.cbrc.kaust.edu.sa/ddmgd/) to provide a comprehensive repository of information related to genes methylated in diseases that can be found through text mining. DDMGD\\'s scope is not limited to a particular group of genes, diseases or species. Using the text mining system DEMGD we developed earlier and additional post-processing, we extracted associations of genes methylated in different diseases from PubMed Central articles and PubMed abstracts. The accuracy of extracted associations is 82% as estimated on 2500 hand-curated entries. DDMGD provides a user-friendly interface facilitating retrieval of these associations ranked according to confidence scores. Submission of new associations to DDMGD is provided. A comparison analysis of DDMGD with several other databases focused on genes methylated in diseases shows that DDMGD is comprehensive and includes most of the recent information on genes methylated in diseases.

False alarms and mine seismicity: An example from the Gentry Mountain mining region, Utah

International Nuclear Information System (INIS)

Taylor, S.R.

1992-01-01

Mining regions are a cause of concern for monitoring of nuclear test ban treaties because they present the opportunity for clandestine nuclear tests (i.e. decoupled explosions). Mining operations are often characterized by high seismicity rates and can provide the cover for excavating voids for decoupling. Chemical explosions (seemingly as part of normal mining activities) can be used to complicate the signals from a simultaneous decoupled nuclear explosion. Thus, most concern about mines has dealt with the issue of missed violations to a test ban treaty. In this study, we raise the diplomatic concern of false alarms associated with mining activities. Numerous reports and papers have been published about anomalous seismicity associated with mining activities. As part of a large discrimination study in the western US (Taylor et al., 1989), we had one earthquake that was consistently classified as an explosion. The magnitude 3.5 disturbance occurred on May 14, 1981 and was conspicuous in its lack of Love waves, relative lack of high- frequency energy, low Lg/Pg ratio, and high m b - M s . A moment-tensor solution by Patton and Zandt (1991) indicated the event had a large implosional component. The event occurred in the Gentry Mountain coal mining region in the eastern Wasatch Plateau, Utah. Using a simple source representation, we modeled the event as a tabular excavation collapse that occurred as a result of normal mining activities. This study raises the importance of having a good catalogue of seismic data and information about mining activities from potential proliferant nations

17 CFR 240.17a-1 - Recordkeeping rule for national securities exchanges, national securities associations...

Science.gov (United States)

2010-04-01

... national securities exchanges, national securities associations, registered clearing agencies and the... Certain Stabilizing Activities § 240.17a-1 Recordkeeping rule for national securities exchanges, national...) Every national securities exchange, national securities association, registered clearing agency and the...

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

Science.gov (United States)

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

Factors Associated With Healthcare-Acquired Catheter-Associated Urinary Tract Infections: Analysis Using Multiple Data Sources and Data Mining Techniques.

Science.gov (United States)

Park, Jung In; Bliss, Donna Z; Chi, Chih-Lin; Delaney, Connie W; Westra, Bonnie L

The purpose of this study was to identify factors associated with healthcare-acquired catheter-associated urinary tract infections (HA-CAUTIs) using multiple data sources and data mining techniques. Three data sets were integrated for analysis: electronic health record data from a university hospital in the Midwestern United States was combined with staffing and environmental data from the hospital's National Database of Nursing Quality Indicators and a list of patients with HA-CAUTIs. Three data mining techniques were used for identification of factors associated with HA-CAUTI: decision trees, logistic regression, and support vector machines. Fewer total nursing hours per patient-day, lower percentage of direct care RNs with specialty nursing certification, higher percentage of direct care RNs with associate's degree in nursing, and higher percentage of direct care RNs with BSN, MSN, or doctoral degree are associated with HA-CAUTI occurrence. The results also support the association of the following factors with HA-CAUTI identified by previous studies: female gender; older age (>50 years); longer length of stay; severe underlying disease; glucose lab results (>200 mg/dL); longer use of the catheter; and RN staffing. Additional findings from this study demonstrated that the presence of more nurses with specialty nursing certifications can reduce HA-CAUTI occurrence. While there may be valid reasons for leaving in a urinary catheter, findings show that having a catheter in for more than 48 hours contributes to HA-CAUTI occurrence. Finally, the findings suggest that more nursing hours per patient-day are related to better patient outcomes.

Critical analysis of the Colombian mining legislation; Analisis critico de la legislacion minera colombiana

Energy Technology Data Exchange (ETDEWEB)

Vargas P, Elkin; Gonzalez S, Carmen Lucia

2003-12-15

The document analyses the Colombian mining legislation, Act 685 of 2001, based on the reasons expressed by the government and the miners for its conceit and approval. The document tries to determine the developments achieved by this new Mining Code considering international mining competitiveness and its adaptation to the constitutional rules about environment, indigenous communities, decentralization and sustainable development. The analysis formulates general and specific hypothesis about the proposed objectives of the reform, which are confronted with the arguments and critical evaluations of the results. Most hypothesis are not verified, thus demonstrating that the Colombian mining legislation is far from being the necessary instrument to promote mining activities, making it competitive according to international standards and adapted to the principles of sustainable development, healthy environment, community participation, ethnic minorities and regional autonomy.

Public health risk assessment associated with heavy metal and arsenic exposure near an abandoned mine (Kirki, Greece).

Science.gov (United States)

Nikolaidis, Christos; Orfanidis, Moysis; Hauri, Dimitri; Mylonas, Stratos; Constantinidis, Theodore

2013-12-01

The 'Agios Philippos' lead-zinc mine in the Kirki region (NE Greece) is now closed, but its legacy of heavy metal contamination remains at the site. At present, management of the contaminated land is of major concern. The area is in a reclamation process and requires immediate remediation action, whereas human risks need to be carefully evaluated. In order to assess these risks, samples from around the mine were collected and analyzed and a scenario involving the oral, dermal, and inhaled doses of arsenic and heavy metals was formulated. A Monte Carlo approach was undertaken, in order to model the average daily dose and quantify the corresponding hazard index and cancer risk. A toxicological risk was associated with samples collected in the vicinity of the mine (floatation, mine tailings) and a pronounced carcinogenic risk for arsenic was evident at the broader occupational/environmental setting. These findings urge for immediate rehabilitation actions that will mitigate population exposures and promote long-term environmental safety in the area.

Associations of Parental Rules and Socioeconomic Position With Preschool Children's Sedentary Behaviour and Screen Time.

Science.gov (United States)

Downing, Katherine L; Hinkley, Trina; Hesketh, Kylie D

2015-04-01

There is little current understanding of the influences on sedentary behavior and screen time in preschool children. This study investigated socioeconomic position (SEP) and parental rules as potential correlates of preschool children's sedentary behavior and screen time. Data from the Healthy Active Preschool Years (HAPPY) Study were used. Participating parents reported their child's usual weekly screen time and their rules to regulate their child's screen time. Children wore accelerometers for 8 days to objectively measure sedentary time. Children whose parents limited television viewing spent significantly less time in that behavior and in total screen time; however, overall sedentary behavior was unaffected. An association between parents limiting computer/electronic game use and time spent on the computer was found for girls only. SEP was inversely associated with girls', but not boys', total screen time and television viewing. As parental rules were generally associated with lower levels of screen time, intervention strategies could potentially encourage parents to set limits on, and switch off, screen devices. Intervention strategies should target preschool children across all SEP areas, as there was no difference by SEP in overall sedentary behavior or screen time for boys.

Zpracování asociačních pravidel metodou vícekriteriálního shlukování

OpenAIRE

Kejkula, Martin

2002-01-01

Association rules mining is one of several ways of knowledge discovery in databases. Paradoxically, data mining itself can produce such great amounts of association rules that there is a new knowledge management problem: there can easily be thousands or even more association rules holding in a data set. The goal of this work is to design a new method for association rules post-processing. The method should be software and domain independent. The output of the new method should be structured d...

Identification of Antimony- and Arsenic-Oxidizing Bacteria Associated with Antimony Mine Tailing

Science.gov (United States)

Hamamura, Natsuko; Fukushima, Koh; Itai, Takaaki

2013-01-01

Antimony (Sb) is a naturally occurring toxic element commonly associated with arsenic (As) in the environment and both elements have similar chemistry and toxicity. Increasing numbers of studies have focused on microbial As transformations, while microbial Sb interactions are still not well understood. To gain insight into microbial roles in the geochemical cycling of Sb and As, soils from Sb mine tailing were examined for the presence of Sb- and As-oxidizing bacteria. After aerobic enrichment culturing with AsIII (10 mM) or SbIII (100 μM), pure cultures of Pseudomonas- and Stenotrophomonas-related isolates with SbIII oxidation activities and a Sinorhizobium-related isolate capable of AsIII oxidation were obtained. The AsIII-oxidizing Sinorhizobium isolate possessed the aerobic arsenite oxidase gene (aioA), the expression of which was induced in the presence of AsIII or SbIII. However, no SbIII oxidation activity was detected from the Sinorhizobium-related isolate, suggesting the involvement of different mechanisms for Sb and As oxidation. These results demonstrate that indigenous microorganisms associated with Sb mine soils are capable of Sb and As oxidation, and potentially contribute to the speciation and mobility of Sb and As in situ. PMID:23666539

Design and Analysis of Decision Rules via Dynamic Programming

KAUST Repository

Amin, Talha M.

2017-04-24

The areas of machine learning, data mining, and knowledge representation have many different formats used to represent information. Decision rules, amongst these formats, are the most expressive and easily-understood by humans. In this thesis, we use dynamic programming to design decision rules and analyze them. The use of dynamic programming allows us to work with decision rules in ways that were previously only possible for brute force methods. Our algorithms allow us to describe the set of all rules for a given decision table. Further, we can perform multi-stage optimization by repeatedly reducing this set to only contain rules that are optimal with respect to selected criteria. One way that we apply this study is to generate small systems with short rules by simulating a greedy algorithm for the set cover problem. We also compare maximum path lengths (depth) of deterministic and non-deterministic decision trees (a non-deterministic decision tree is effectively a complete system of decision rules) with regards to Boolean functions. Another area of advancement is the presentation of algorithms for constructing Pareto optimal points for rules and rule systems. This allows us to study the existence of “totally optimal” decision rules (rules that are simultaneously optimal with regards to multiple criteria). We also utilize Pareto optimal points to compare and rate greedy heuristics with regards to two criteria at once. Another application of Pareto optimal points is the study of trade-offs between cost and uncertainty which allows us to find reasonable systems of decision rules that strike a balance between length and accuracy.

Mine drivage in hydraulic mines

Energy Technology Data Exchange (ETDEWEB)

Ehkber, B Ya

1983-09-01

From 20 to 25% of labor cost in hydraulic coal mines falls on mine drivage. Range of mine drivage is high due to the large number of shortwalls mined by hydraulic monitors. Reducing mining cost in hydraulic mines depends on lowering drivage cost by use of new drivage systems or by increasing efficiency of drivage systems used at present. The following drivage methods used in hydraulic mines are compared: heading machines with hydraulic haulage of cut rocks and coal, hydraulic monitors with hydraulic haulage, drilling and blasting with hydraulic haulage of blasted rocks. Mining and geologic conditions which influence selection of the optimum mine drivage system are analyzed. Standardized cross sections of mine roadways driven by the 3 methods are shown in schemes. Support systems used in mine roadways are compared: timber supports, roof bolts, roof bolts with steel elements, and roadways driven in rocks without a support system. Heading machines (K-56MG, GPKG, 4PU, PK-3M) and hydraulic monitors (GMDTs-3M, 12GD-2) used for mine drivage are described. Data on mine drivage in hydraulic coal mines in the Kuzbass are discussed. From 40 to 46% of roadways are driven by heading machines with hydraulic haulage and from 12 to 15% by hydraulic monitors with hydraulic haulage.

Frac Sand Mines Are Preferentially Sited in Unzoned Rural Areas.

Science.gov (United States)

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

Personnel Audit Using a Forensic Mining Technique

OpenAIRE

Adesesan B. Adeyemo; Oluwafemi Oriola

2010-01-01

This paper applies forensic data mining to determine the true status of employees and thereafter provide useful evidences for proper administration of administrative rules in a Typical Nigerian Teaching Service. The conventional technique of personnel audit was studied and a new technique for personnel audit was modeled using Artificial Neural Networks and Decision Tree algorithms. Atwo-layer classifier architecture was modeled. The outcome of the experiment proved that Radial Basis Function ...

Mechanisms of rule acquisition and rule following in inductive reasoning.

Science.gov (United States)

Crescentini, Cristiano; Seyed-Allaei, Shima; De Pisapia, Nicola; Jovicich, Jorge; Amati, Daniele; Shallice, Tim

2011-05-25

Despite the recent interest in the neuroanatomy of inductive reasoning processes, the regional specificity within prefrontal cortex (PFC) for the different mechanisms involved in induction tasks remains to be determined. In this study, we used fMRI to investigate the contribution of PFC regions to rule acquisition (rule search and rule discovery) and rule following. Twenty-six healthy young adult participants were presented with a series of images of cards, each consisting of a set of circles numbered in sequence with one colored blue. Participants had to predict the position of the blue circle on the next card. The rules that had to be acquired pertained to the relationship among succeeding stimuli. Responses given by subjects were categorized in a series of phases either tapping rule acquisition (responses given up to and including rule discovery) or rule following (correct responses after rule acquisition). Mid-dorsolateral PFC (mid-DLPFC) was active during rule search and remained active until successful rule acquisition. By contrast, rule following was associated with activation in temporal, motor, and medial/anterior prefrontal cortex. Moreover, frontopolar cortex (FPC) was active throughout the rule acquisition and rule following phases before a rule became familiar. We attributed activation in mid-DLPFC to hypothesis generation and in FPC to integration of multiple separate inferences. The present study provides evidence that brain activation during inductive reasoning involves a complex network of frontal processes and that different subregions respond during rule acquisition and rule following phases.

Wetland and waterbody restoration and creation associated with mining

International Nuclear Information System (INIS)

Brooks, R.P.

1990-01-01

Published and unpublished reports are reviewed and the strategies and techniques used to facilitate the establishment of wetlands and waterbodies during mine reclamation are summarized. Although the emphasis is on coal, phosphate, and sand and gravel operations, the methods are relevant to other types of mining and mitigation activities. The following key points should receive attention during planning and mitigation processes: (1) development of site-specific objectives that are related to regional wetland trends; (2) integration of wetland mitigation plans with mining operations and reclamation at the beginning of any project; (3) wetland designs that mimic natural systems and provide flexibility for unforeseen events; (4) assurance that basin morphometry and control of the hydrologic regime are properly addressed before considering other aspects of a project; and (5) identification of mandatory monitoring as a known cost. Well-designed studies that use comparative approaches are needed to increase the database on wetland restoration technology. Meanwhile, regional success criteria for different classes of wetlands need to be developed by consensus agreement among professionals. The rationale for a particular mitigation strategy must have a sound, scientific basis if the needs of mining industries are to be balanced against the necessity of wetland operation. 93 refs., 3 figs

Rule-guided human classification of Volunteered Geographic Information

Science.gov (United States)

Ali, Ahmed Loai; Falomir, Zoe; Schmid, Falko; Freksa, Christian

2017-05-01

During the last decade, web technologies and location sensing devices have evolved generating a form of crowdsourcing known as Volunteered Geographic Information (VGI). VGI acted as a platform of spatial data collection, in particular, when a group of public participants are involved in collaborative mapping activities: they work together to collect, share, and use information about geographic features. VGI exploits participants' local knowledge to produce rich data sources. However, the resulting data inherits problematic data classification. In VGI projects, the challenges of data classification are due to the following: (i) data is likely prone to subjective classification, (ii) remote contributions and flexible contribution mechanisms in most projects, and (iii) the uncertainty of spatial data and non-strict definitions of geographic features. These factors lead to various forms of problematic classification: inconsistent, incomplete, and imprecise data classification. This research addresses classification appropriateness. Whether the classification of an entity is appropriate or inappropriate is related to quantitative and/or qualitative observations. Small differences between observations may be not recognizable particularly for non-expert participants. Hence, in this paper, the problem is tackled by developing a rule-guided classification approach. This approach exploits data mining techniques of Association Classification (AC) to extract descriptive (qualitative) rules of specific geographic features. The rules are extracted based on the investigation of qualitative topological relations between target features and their context. Afterwards, the extracted rules are used to develop a recommendation system able to guide participants to the most appropriate classification. The approach proposes two scenarios to guide participants towards enhancing the quality of data classification. An empirical study is conducted to investigate the classification of grass

Exploring Characterizations of Learning Object Repositories Using Data Mining Techniques

Science.gov (United States)

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.

DDMGD: the database of text-mined associations between genes methylated in diseases from different species.

Science.gov (United States)

Bin Raies, Arwa; Mansour, Hicham; Incitti, Roberto; Bajic, Vladimir B

2015-01-01

Gathering information about associations between methylated genes and diseases is important for diseases diagnosis and treatment decisions. Recent advancements in epigenetics research allow for large-scale discoveries of associations of genes methylated in diseases in different species. Searching manually for such information is not easy, as it is scattered across a large number of electronic publications and repositories. Therefore, we developed DDMGD database (http://www.cbrc.kaust.edu.sa/ddmgd/) to provide a comprehensive repository of information related to genes methylated in diseases that can be found through text mining. DDMGD's scope is not limited to a particular group of genes, diseases or species. Using the text mining system DEMGD we developed earlier and additional post-processing, we extracted associations of genes methylated in different diseases from PubMed Central articles and PubMed abstracts. The accuracy of extracted associations is 82% as estimated on 2500 hand-curated entries. DDMGD provides a user-friendly interface facilitating retrieval of these associations ranked according to confidence scores. Submission of new associations to DDMGD is provided. A comparison analysis of DDMGD with several other databases focused on genes methylated in diseases shows that DDMGD is comprehensive and includes most of the recent information on genes methylated in diseases. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Fuzzy-logic assessment of failure hazard in pipelines due to mining activity

Directory of Open Access Journals (Sweden)

A. A. Malinowska

2015-11-01

Full Text Available The present research is aimed at a critical analysis of a method presently used for evaluating failure hazard in linear objects in mining areas. A fuzzy model of failure hazard of a linear object was created on the basis of the experience gathered so far. The rules of Mamdani fuzzy model have been used in the analyses. Finally the scaled model was integrated with a Geographic Information System (GIS, which was used to evaluate failure hazard in a water pipeline in a mining area.

Reduce of adherence problems in galvanised processes through data mining techniques

International Nuclear Information System (INIS)

Martinez de Pison, F. J.; Ordieres, J.; Pernia, A.; Alba, F.; Torre, V.

2007-01-01

This paper presents an example of the application of data mining techniques to obtain hidden knowledge from the historical data of a hot dip galvanizing process and to establish rules to improve quality in the final product and to reduce errors in the process. For this purpose, the tuning records of a hot dip galvanizing line where coils with adherence problems in the zinc coating had been identified were used as starting point. From the database of the process, the classical data mining approach was applied to obtain and analyze a number of decision trees hat classified two types of coils, i.e. those with the right adherence and those with irregular adherence. The variables and values that might have influenced the quality of the coating were extracted from these tress. Several rules that may be applied to reduce the number of faulty coils with adherence problems were also established. (Author) 24 refs

An unsupervised text mining method for relation extraction from biomedical literature.

Directory of Open Access Journals (Sweden)

Changqin Quan

Full Text Available The wealth of interaction information provided in biomedical articles motivated the implementation of text mining approaches to automatically extract biomedical relations. This paper presents an unsupervised method based on pattern clustering and sentence parsing to deal with biomedical relation extraction. Pattern clustering algorithm is based on Polynomial Kernel method, which identifies interaction words from unlabeled data; these interaction words are then used in relation extraction between entity pairs. Dependency parsing and phrase structure parsing are combined for relation extraction. Based on the semi-supervised KNN algorithm, we extend the proposed unsupervised approach to a semi-supervised approach by combining pattern clustering, dependency parsing and phrase structure parsing rules. We evaluated the approaches on two different tasks: (1 Protein-protein interactions extraction, and (2 Gene-suicide association extraction. The evaluation of task (1 on the benchmark dataset (AImed corpus showed that our proposed unsupervised approach outperformed three supervised methods. The three supervised methods are rule based, SVM based, and Kernel based separately. The proposed semi-supervised approach is superior to the existing semi-supervised methods. The evaluation on gene-suicide association extraction on a smaller dataset from Genetic Association Database and a larger dataset from publicly available PubMed showed that the proposed unsupervised and semi-supervised methods achieved much higher F-scores than co-occurrence based method.

Profiling of high-frequency accident locations by use of association rules

OpenAIRE

GEURTS, Karolien; WETS, Geert; BRIJS, Tom; VANHOOF, Koen

2003-01-01

In Belgium, traffic safety is one of the government's highest priorities. The identification and profiling of black spots and black zones (geographical locations with high concentrations of traffic accidents) in terms of accident-related data and location characteristics must provide new insights into the complexity and causes of road accidents, which, in turn, provide valuable input for governmental actions. Association rules were used to identify accident-related circumstances that frequent...

Development of the testing procedure for units and elements of mining equipment

Directory of Open Access Journals (Sweden)

P. B. Gerike

2017-09-01

Full Text Available The author considers in detail the stages of creating a testing procedure for mining equipment based on the complex implementation of principles of nondestructive testing and technical diagnostics. The author substantiates effectiveness of application of a complex diagnostic approach for assessing the state of metal structures and energy-mechanical equipment of mining machines. The opportunity for timely detection of defects, regardless of their type and degree of danger, presents itself only with a wide application of the modern methods of vibration diagnostics and nondestructive testing. The author substantiates the effectiveness of specific combination of methods of nondestructive testing, most optimally suited for solving given tasks. The article contains the developed complex of more than 120 diagnostic rules, suitable for performing automated analysis of vibroacoustic signal and revealing the main damages of energy-mechanical equipment based on selective groups of informative frequencies. The author formulates the main criteria that one can use as a basic platform for improving the methodology for normalizing the parameters of mechanical oscillations. The developed diagnostic criteria became a basis for the development of individual spectral masks suitable for performing the analysis of parameters of vibroacoustic waves generated during operation of mining equipment. The author proves necessity of transition of repair and maintenance divisions of industrial enterprises to the system of maintenance of machinery according to its actual technical state, and the developed complex of diagnostic rules for detecting defects can serve as a platform for the implementation of basic elements of this system. The author substantiates the principal validity of the developed methodology for testing mining machines equipment and its individual elements, such as the predictive modeling of degradation of technical state of mining equipment and the

Characterization of iron and manganese minerals and their associated microbiota in different mine sites to reveal the potential interactions of microbiota with mineral formation.

Science.gov (United States)

Park, Jin Hee; Kim, Bong-Soo; Chon, Chul-Min

2018-01-01

Different environmental conditions such as pH and dissolved elements of mine stream induce precipitation of different minerals and their associated microbial community may vary. Therefore, mine precipitates from various environmental conditions were collected and their associated microbiota were analyzed through metagenomic DNA sequencing. Various Fe and Mn minerals including ferrihydrite, schwertmannite, goethite, birnessite, and Mn-substituted δ-FeOOH (δ-(Fe 1-x , Mn x )OOH) were found in the different environmental conditions. The Fe and Mn minerals were enriched with toxic metal(loid)s including As, Cd, Ni and Zn, indicating they can act as scavengers of toxic metal(loid)s in mine streams. Under acidic conditions, Acidobacteria was dominant phylum and Gallionella (Fe oxidizing bacteria) was the predominant genus in these Fe rich environments. Manganese oxidizing bacteria, Hyphomicrobium, was found in birnessite forming environments. Leptolyngbya within Cyanobacteria was found in Fe and Mn oxidizing environments, and might contribute to Fe and Mn oxidation through the production of molecular oxygen. The potential interaction of microbial community with minerals in mine sites can be traced by analysis of microbial community in different Fe and Mn mineral forming environments. Iron and Mn minerals contribute to the removal of toxic metal(loid)s from mine water. Therefore, the understanding characteristics of mine precipitates and their associated microbes helps to develop strategies for the management of contaminated mine water. Copyright © 2017 Elsevier Ltd. All rights reserved.

Stratified sampling design based on data mining.

Science.gov (United States)

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.

Classification of Internet banking customers using data mining algorithms

Directory of Open Access Journals (Sweden)

Reza Radfar

2014-03-01

Full Text Available Classifying customers using data mining algorithms, enables banks to keep old customers loyality while attracting new ones. Using decision tree as a data mining technique, we can optimize customer classification provided that the appropriate decision tree is selected. In this article we have presented an appropriate model to classify customers who use internet banking service. The model is developed based on CRISP-DM standard and we have used real data of Sina bank’s Internet bank. In compare to other decision trees, ours is based on both optimization and accuracy factors that recognizes new potential internet banking customers using a three level classification, which is low/medium and high. This is a practical, documentary-based research. Mining customer rules enables managers to make policies based on found out patterns in order to have a better perception of what customers really desire.

Reclamation of slopes left after surface mining

Energy Technology Data Exchange (ETDEWEB)

Zmitko, J [Banske Projekty, Teplice (Czech Republic)

1993-03-01

Discusses land reclamation of abandoned slopes from brown coal surface mining in the North Bohemian brown coal basin in the Czech Republic. Problems associated with reclamation of landslide areas in two former coal mines are evaluated: the Otokar mine in Kostany (mining from 1956 to 1966) and the CSM mine in Pozorka (mining from 1955 to 1967). Land reclamation was introduced 25 years after damage occurred. The following aspects are analyzed: hydrogeologic conditions, range of landslides, types of rocks in landslide areas, water conditions, methods for stabilizing slopes, safety aspects.

Health concerns associated with unconventional gas mining in rural Australia.

Science.gov (United States)

Haswell, Melissa R; Bethmont, Anna

2016-01-01

Many governments globally are investigating the benefits and risks associated with unconventional gas mining for shale, tight and coal seam gas (coalbed methane) to determine whether the industry should proceed in their jurisdiction. Most locations likely to be developed are in rural areas, with potential impact on farmers and small communities. Despite significant health concerns, public health knowledge and growing evidence are often overlooked in decision-making. It is difficult to gain a broad but accurate understanding of the health concerns for rural communities because the evidence has grown very recently and rapidly, is complex and largely based in the USA, where the industry is advanced. In 2016, a concerned South Australian beef and lamb farmer in an area targeted for potential unconventional gas development organised visits to homes in developed unconventional gas areas of Pennsylvania and forums with leading researchers and lawyers in Pennsylvania and New York. Guided by priorities identified during this trip, this communication concisely distils the research evidence on these key concerns, highlighting the Australian situation where evidence exists. It summarises key information of particular concern to rural regions, using Australia as an example, to assist rural health professionals to be better prepared to engage in decision-making and address the challenges associated with this new industry. Discussions with communities and experts, supported by the expanding research from the USA and Australia, revealed increasing health concerns in six key areas. These are absence of a safe solution to the toxic wastewater management problems, air pollution, land and water competition, mental health and psychosocial wellbeing risks, fugitive methane emissions and lack of proven regulatory regimes. Emerging epidemiological studies suggesting interference with foetal development and birth outcomes, and exacerbation of asthma conditions, are particularly concerning

Applying Data-mining techniques to study drought periods in Spain

Science.gov (United States)

Belda, F.; Penades, M. C.

2010-09-01

Data-mining is a technique that it can be used to interact with large databases and to help in the discovery relations between parameters by extracting information from massive and multiple data archives. Drought affects many economic and social sectors, from agricultural to transportation, going through urban water deficit and the development of modern industries. With these problems and drought geographical and temporal distribution it's difficult to find a single definition of drought. Improving the understanding of the knowledge of climatic index is necessary to reduce the impacts of drought and to facilitate quick decisions regarding this problem. The main objective is to analyze drought periods from 1950 to 2009 in Spain. We use several kinds of information, different formats, sources and transmission mode. We use satellite-based Vegetation Index, dryness index for several temporal periods. We use daily and monthly precipitation and temperature data and soil moisture data from numerical weather model. We calculate mainly Standardized Precipitation Index (SPI) that it has been used amply in the bibliography. We use OLAP-Mining techniques to discovery of association rules between remote-sensing, numerical weather model and climatic index. Time series Data- Mining techniques organize data as a sequence of events, with each event having a time of recurrence, to cluster the data into groups of records or cluster with similar characteristics. Prior climatological classification is necessary if we want to study drought periods over all Spain.

Mining Trust Relationships from Online Social Networks

Institute of Scientific and Technical Information of China (English)

Yu Zhang; Tong Yu

2012-01-01

With the growing popularity of online social network,trust plays a more and more important role in connecting people to each other.We rely on our personal trust to accept recommendations,to make purchase decisions and to select transaction partners in the online community.Therefore,how to obtain trust relationships through mining online social networks becomes an important research topic.There are several shortcomings of existing trust mining methods.First,trust is category-dependent.However,most of the methods overlook the category attribute of trust relationships,which leads to low accuracy in trust calculation.Second,since the data in online social networks cannot be understood and processed by machines directly,traditional mining methods require much human effort and are not easily applied to other applications.To solve the above problems,we propose a semantic-based trust reasoning mechanism to mine trust relationships from online social networks automatically.We emphasize the category attribute of pairwise relationships and utilize Semantic Web technologies to build a domain ontology for data communication and knowledge sharing.We exploit role-based and behavior-based reasoning functions to infer implicit trust relationships and category-specific trust relationships.We make use of path expressions to extend reasoning rules so that the mining process can be done directly without much human effort.We perform experiments on real-life data extracted from Epinions.The experimental results verify the effectiveness and wide application use of our proposed method.

Privacy-preserving distributed mining of association rules using ...

Indian Academy of Sciences (India)

Harendra Chahar

2017-11-17

Nov 17, 2017 ... cally or vice-versa. For market analysis, business organizations would like to ..... ticipating sites using Elliptic-curve-based digital signature algorithm. ..... nication Technologies, Research, Innovation, and Vision for the Future ...

The N-terminal amphipathic helix of the topological specificity factor MinE is associated with shaping membrane curvature.

Directory of Open Access Journals (Sweden)

Yu-Ling Shih

Full Text Available Pole-to-pole oscillations of the Min proteins in Escherichia coli are required for the proper placement of the division septum. Direct interaction of MinE with the cell membrane is critical for the dynamic behavior of the Min system. In vitro, this MinE-membrane interaction led to membrane deformation; however, the underlying mechanism remained unclear. Here we report that MinE-induced membrane deformation involves the formation of an amphipathic helix of MinE(2-9, which, together with the adjacent basic residues, function as membrane anchors. Biochemical evidence suggested that the membrane association induces formation of the helix, with the helical face, consisting of A2, L3, and F6, inserted into the membrane. Insertion of this helix into the cell membrane can influence local membrane curvature and lead to drastic changes in membrane topology. Accordingly, MinE showed characteristic features of protein-induced membrane tubulation and lipid clustering in in vitro reconstituted systems. In conclusion, MinE shares common protein signatures with a group of membrane trafficking proteins in eukaryotic cells. These MinE signatures appear to affect membrane curvature.

Construction and modernization of underground and surface mines. [USSR

Energy Technology Data Exchange (ETDEWEB)

Burshtein, N M

1983-12-01

Development of the Sredazugol' association in Soviet Central Asia from 1976 to 1985 is discussed. From 1976 to 1980 investment in the association amounted to 151 million rubles, 87.5 million of which fell on construction. Major development projects of the 1976-1980 period are reviewed: construction of new mining levels in underground coal mines, development of a number of operating surface mines, modernization of earthmoving and mining equipment, development of mine haulage by locomotives and railroad cars, improving occupational safety in coal mining, increasing slope stability in surface mining, especially in the area of the Atchinsk landslide in the Angren mine. From 1981 to 1985 investment in the Sredazugol' association should amount to 202 million rubles, of which 126 million rubles will be spent on construction. Investment will be 35% higher than in the 1976-1980 period and investment in mine construction 43% higher. The largest development project will be modernization of the Angren surface mine and increasing its targeted coal output from 5.2 Mt/y to 10.3 Mt/y by 1990. Modernization and reconstruction of the Angren mine will be carried out in 2 stages. Coal output of the mine will increase by 1.2 Mt/y in the current 5 year plan (by 1985), and by 3.9 Mt/y in the next 5 year period. Reconstruction and development of the Angren mine will cost approximately 254 million rubles. Mining and earthmoving equipment which will be used in the Angren mine is reviewed: EhRGV-630 bucket wheel excavators, EhSh-10/70 and EhSh-13/50 walking draglines, etc.

Mining and mining authorities in Saarland 2016. Mining economy, mining technology, occupational safety, environmental protection, statistics, mining authority activities. Annual report

International Nuclear Information System (INIS)

2016-01-01

The annual report of the Saarland Upper Mining Authority provides an insight into the activities of mining authorities. Especially, the development of the black coal mining, safety and technology of mining as well as the correlation between mining and environment are stressed.

ABOUT CLINICAL EXPERT SYSTEM BASED ON RULES USING DATA MINING TECHNOLOGY

Directory of Open Access Journals (Sweden)

V. P. Martsenyuk

2015-05-01

Full Text Available In the work the topics of software implementation of rule induction method based on sequential covering algorithm are considered. Such approach allows us to develop clinical decision support system. The project is implemented within Netbeans IDE based on Java-classes.

GWA study data mining and independent replication identify cardiomyopathy-associated 5 (CMYA5) as a risk gene for schizophrenia

NARCIS (Netherlands)

Chen, X.; Lee, G.; Maher, B. S.; Fanous, A. H.; Chen, J.; Zhao, Z.; Guo, A.; van den Oord, E.; Sullivan, P. F.; Shi, J.; Levinson, D. F.; Gejman, P. V.; Sanders, A.; Duan, J.; Owen, M. J.; Craddock, N. J.; O'Donovan, M. C.; Blackman, J.; Lewis, D.; Kirov, G. K.; Qin, W.; Schwab, S.; Wildenauer, D.; Chowdari, K.; Nimgaonkar, V.; Straub, R. E.; Weinberger, D. R.; O'Neill, F. A.; Walsh, D.; Bronstein, M.; Darvasi, A.; Lencz, T.; Malhotra, A. K.; Rujescu, D.; Giegling, I.; Werge, T.; Hansen, T.; Ingason, A.; Nöethen, M. M.; Rietschel, M.; Cichon, S.; Djurovic, S.; Andreassen, O. A.; Cantor, R. M.; Ophoff, R.; Corvin, A.; Morris, D. W.; Gill, M.; Pato, C. N.; Pato, M. T.; Macedo, A.; Gurling, H. M. D.; McQuillin, A.; Pimm, J.; Hultman, C.; Lichtenstein, P.; Sklar, P.; Purcell, S. M.; Scolnick, E.; St Clair, D.; Blackwood, D. H. R.; Kendler, K. S.; Kahn, René S.; Linszen, Don H.; van Os, Jim; Wiersma, Durk; Bruggeman, Richard; Cahn, Wiepke; de Haan, Lieuwe; Krabbendam, Lydia; Myin-Germeys, Inez; O'Donovan, Michael C.; Kirov, George K.; Craddock, Nick J.; Holmans, Peter A.; Williams, Nigel M.; Georgieva, Lyudmila; Nikolov, Ivan; Norton, N.; Williams, H.; Toncheva, Draga; Milanova, Vihra; Owen, Michael J.; Hultman, Christina M.; Lichtenstein, Paul; Thelander, Emma F.; Sullivan, Patrick; Morris, Derek W.; O'Dushlaine, Colm T.; Kenny, Elaine; Quinn, Emma M.; Gill, Michael; Corvin, Aiden; McQuillin, Andrew; Choudhury, Khalid; Datta, Susmita; Pimm, Jonathan; Thirumalai, Srinivasa; Puri, Vinay; Krasucki, Robert; Lawrence, Jacob; Quested, Digby; Bass, Nicholas; Gurling, Hugh; Crombie, Caroline; Fraser, Gillian; Kuan, Soh Leh; Walker, Nicholas; St Clair, David; Blackwood, Douglas H. R.; Muir, Walter J.; McGhee, Kevin A.; Pickard, Ben; Malloy, Pat; Maclean, Alan W.; van Beck, Margaret; Wray, Naomi R.; Macgregor, Stuart; Visscher, Peter M.; Pato, Michele T.; Medeiros, Helena; Middleton, Frank; Carvalho, Celia; Morley, Christopher; Fanous, Ayman; Conti, David; Knowles, James A.; Ferreira, Carlos Paz; Macedo, Antonio; Azevedo, M. Helena; Pato, Carlos N.; Stone, Jennifer L.; Ruderfer, Douglas M.; Kirby, Andrew N.; Ferreira, Manuel A. R.; Daly, Mark J.; Purcell, Shaun M.; Sklar, Pamela; Chambert, Kimberly; Kuruvilla, Finny; Gabriel, Stacey B.; Ardlie, Kristin; Moran, Jennifer L.; Scolnick, Edward M.

2011-01-01

We conducted data-mining analyses using the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) and molecular genetics of schizophrenia genome-wide association study supported by the genetic association information network (MGS-GAIN) schizophrenia data sets and performed

Local zoning ordinances -- how they limit or restrict mining

International Nuclear Information System (INIS)

Ingram, H.

1991-01-01

Local regulation of mining by zoning has taken place for a long period of time. The delegation to local municipalities of land use planning, zoning and nuisance abatement authority which may affect mining by state governments has been consistently upheld by appellate courts as valid exercises of the police power. Recently, mine operators and mineral owners have been confronted by efforts of local municipalities, often initiated by anti-mining citizen's groups, to impose more stringent restrictions on mining activities within their borders. In some situations, existing ordinances are being enforced for the first time, in others, new ordinances have been adopted without much awareness or involvement by the public. Enforced to the letter, these ordinances can sterilize large blocks of mineable reserves open-quotes operatingclose quotes or performance standards in excess of SMCRA-based regulatory requirements. It is fair to say that investigation of the potential impacts of local zoning and other related ordinances is essential in the planning for the expansion of existing operations or for new operations. There may be new rules in the game. This paper identifies problem areas in typical open-quotes modernclose quotes ordinances and discusses legal and constitutional issues which may arise by their enforcement in coal producing regions

Mine-induced seismicity at East-Rand proprietary mines

CSIR Research Space (South Africa)

Milev, AM

1995-09-01

Full Text Available Mining results in seismic activity of varying intensity, from small micro seismic events to larger seismic events, often associated with significant seismic induced damages. This work deals with the understanding of the present seismicity...

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

Science.gov (United States)

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.

Science.gov (United States)

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.

Effects of natural conditions on development of a landslide at a mining area

Energy Technology Data Exchange (ETDEWEB)

Palki, J

1982-01-01

Investigations show that 39.4% of landslides at the mining ground in the Rybnik coal region is caused by factors other than underground mining. Determining the actual cause of a landslide is of importance for an underground mine because, as a rule, all ground damage is claimed to be caused by underground mining. A case of a landslide in the Rybnik area is analyzed. Landslide development is shown in a photo and 17 schemes. Ground morphology, water conditions and effects of underground longwall mining are evaluated. The analyses show that ground subsidence caused by underground mining ranging from 2.0 m to 3.1 m reduced the angle of slope inclination preventing more intensive landslides. Intensity of horizontal deformation was too low to cause a landslide. Slope stability decrease was caused by loose rock layers (sands) at the base of a hill. Accumulation of atmospheric precipitation and disturbed water outflow caused an increase in the plasticity of the sand. Mechanical vibrations caused by train traffic on tracks located close to the slope were an additional factor causing landslide development. (6 refs.)

Uranium mining sites - Thematic sheets

International Nuclear Information System (INIS)

2009-01-01

A first sheet proposes comments, data and key numbers about uranium extraction in France: general overview of uranium mining sites, status of waste rock and tailings after exploitation, site rehabilitation. The second sheet addresses the sources of exposure to ionizing radiations due to ancient uranium mining sites: discussion on the identification of these sources associated with these sites, properly due to mining activities or to tailings, or due to the transfer of radioactive substances towards water and to the contamination of sediments, description of the practice and assessment of radiological control of mining sites. A third sheet addresses the radiological exposure of public to waste rocks, and the dose assessment according to exposure scenarios: main exposure ways to be considered, studied exposure scenarios (passage on backfilled path and grounds, stay in buildings built on waste rocks, keeping mineralogical samples at home). The fourth sheet addresses research programmes of the IRSN on uranium and radon: epidemiological studies (performed on mine workers; on French and on European cohorts, French and European studies on the risk of lung cancer associated with radon in housing), study of the biological effects of chronic exposures. The last sheet addresses studies and expertises performed by the IRSN on ancient uranium mining sites in France: studies commissioned by public authorities, radioactivity control studies performed by the IRSN about mining sites, participation of the IRSN to actions to promote openness to civil society

Data mining applications in healthcare.

Science.gov (United States)

Koh, Hian Chye; Tan, Gerald

2005-01-01

Data mining has been used intensively and extensively by many organizations. In healthcare, data mining is becoming increasingly popular, if not increasingly essential. Data mining applications can greatly benefit all parties involved in the healthcare industry. For example, data mining can help healthcare insurers detect fraud and abuse, healthcare organizations make customer relationship management decisions, physicians identify effective treatments and best practices, and patients receive better and more affordable healthcare services. The huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. This article explores data mining applications in healthcare. In particular, it discusses data mining and its applications within healthcare in major areas such as the evaluation of treatment effectiveness, management of healthcare, customer relationship management, and the detection of fraud and abuse. It also gives an illustrative example of a healthcare data mining application involving the identification of risk factors associated with the onset of diabetes. Finally, the article highlights the limitations of data mining and discusses some future directions.

Granular-relational data mining how to mine relational data in the paradigm of granular computing ?

CERN Document Server

Hońko, Piotr

2017-01-01

This book provides two general granular computing approaches to mining relational data, the first of which uses abstract descriptions of relational objects to build their granular representation, while the second extends existing granular data mining solutions to a relational case. Both approaches make it possible to perform and improve popular data mining tasks such as classification, clustering, and association discovery. How can different relational data mining tasks best be unified? How can the construction process of relational patterns be simplified? How can richer knowledge from relational data be discovered? All these questions can be answered in the same way: by mining relational data in the paradigm of granular computing! This book will allow readers with previous experience in the field of relational data mining to discover the many benefits of its granular perspective. In turn, those readers familiar with the paradigm of granular computing will find valuable insights on its application to mining r...

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

Science.gov (United States)

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.

78 FR 45051 - Small Business Size Standards; Support Activities for Mining; Correction

Science.gov (United States)

2013-07-26

... Regulations by increasing small business size standards for three of the four industries in North American... SMALL BUSINESS ADMINISTRATION 13 CFR Part 121 RIN 3245-AG44 Small Business Size Standards; Support Activities for Mining; Correction AGENCY: U.S. Small Business Administration. ACTION: Final rule; correction...

Frac Sand Mines Are Preferentially Sited in Unzoned Rural Areas.

Directory of Open Access Journals (Sweden)

Christina Locke

Full Text Available 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

Is outdoor work associated with elevated rates of cerebrovascular disease mortality? : a cohort study based on iron-ore mining

OpenAIRE

Björ, Ove; Jonsson, Håkan; Damber, Lena; Burström, Lage; Nilsson, Tohr

2016-01-01

BACKGROUND: A cohort study that examined iron ore mining found negative associations between cumulative working time employed underground and several outcomes, including mortality of cerebrovascular diseases. In this cohort study, and using the same group of miners, we examined whether work in an outdoor environment could explain elevated cerebrovascular disease rates. METHODS: This study was based on a Swedish iron ore mining cohort consisting of 13,000 workers. Poisson regression models wer...

Soft measures and incremental gains in mines; Mesures douces et gains incrementaux : mines

Energy Technology Data Exchange (ETDEWEB)

Laliberte, P. [Natural Resources Canada, Ottawa, ON (Canada). CANMET Mining and Mineral Sciences Laboratories

2008-07-01

This paper presented a variety of measures that mine operators can adopt to save energy. Researchers at the CANMET Mining and Mineral Sciences Laboratories of Natural Resources Canada have conducted a joint study with Hydro-Quebec to investigate the impact of alternate energy technologies and control systems on energy savings. The impacts of a range of technologies were evaluated and rates of energy efficiency were compared. Technologies included hybrid vehicles; fuel cell-powered vehicles; automated ventilation control systems; heat recovery; compressed air; and electrical mining equipment. Energy profiles for various industrial applications were included. This paper also provided details of computerized simulations currently being conducted to estimate the potential incremental gains associated with the use of technology innovations in mining applications. 9 tabs., 3 figs.

Application of EREP imagery to fracture-related mine safety hazards and environmental problems in mining

Science.gov (United States)

Wier, C. E.; Wobber, F. J.; Amato, R. V.; Russell, O. R. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Numerous fracture traces were detected on both the color transparencies and black and white spectral bands. Fracture traces of value to mining hazards analysis were noted on the EREP imagery which could not be detected on either the ERTS-1 or high altitude aircraft color infrared photography. Several areas of mine subsidence occurring in the Busseron Creek area near Sullivan, Indiana were successfully identified using color photography. Skylab photography affords an increase over comparable scale ERTS-1 imagery in level of information obtained in mined lands inventory and reclamation analysis. A review of EREP color photography permitted the identification of a substantial number of non-fuel mines within the Southern Indiana test area. A new mine was detected on the EREP photography without prior data. EREP has definite value for estimating areal changes in active mines and for detecting new non-fuel mines. Gob piles and slurry ponds of several acres could be detected on the S-190B color photography when observed in association with large scale mining operations. Apparent degradation of water quality resulting from acid mine drainage and/or siltation was noted in several ponds or small lakes and appear to be related to intensive mining activity near Sullivan, Indiana.

Discovering approximate-associated sequence patterns for protein-DNA interactions

KAUST Repository