Dawn E. Holmes and Lakhmi C. Jain (Eds.) Data Mining: Foundations and Intelligent Paradigms

Transcription

1 Dawn E. Holmes and Lakhmi C. Jain (Eds.) Data Mining: Foundations and Intelligent Paradigms

2 Intelligent Systems Reference Library, Volume 25 Editors-in-Chief Prof. Janusz Kacprzyk Systems Research Institute Polish Academy of Sciences ul. Newelska Warsaw Poland kacprzyk@ibspan.waw.pl Prof.LakhmiC.Jain University of South Australia Adelaide Mawson Lakes Campus South Australia 5095 Australia Lakhmi.jain@unisa.edu.au Further volumes of this series can be found on our homepage: springer.com Vol. 1. Christine L. Mumford and Lakhmi C. Jain (Eds.) Computational Intelligence: Collaboration, Fusion and Emergence, 2009 ISBN Vol. 2.Yuehui Chen and Ajith Abraham Tree-Structure Based Hybrid Computational Intelligence, 2009 ISBN Vol. 3. Anthony Finn and Steve Scheding Developments and Challenges for Autonomous Unmanned Vehicles, 2010 ISBN Vol. 4. Lakhmi C. Jain and Chee Peng Lim (Eds.) Handbook on Decision Making: Techniques and Applications, 2010 ISBN Vol. 5. George A. Anastassiou Intelligent Mathematics: Computational Analysis, 2010 ISBN Vol. 6. Ludmila Dymowa Soft Computing in Economics and Finance, 2011 ISBN Vol. 7. Gerasimos G. Rigatos Modelling and Control for Intelligent Industrial Systems, 2011 ISBN Vol. 8. Edward H.Y. Lim, James N.K. Liu, and Raymond S.T. Lee Knowledge Seeker Ontology Modelling for Information Search and Management, 2011 ISBN Vol. 9. Menahem Friedman and Abraham Kandel Calculus Light, 2011 ISBN Vol. 10. Andreas Tolk and Lakhmi C. Jain Intelligence-Based Systems Engineering, 2011 ISBN Vol. 11. Samuli Niiranen and Andre Ribeiro (Eds.) Information Processing and Biological Systems, 2011 ISBN Vol. 12. Florin Gorunescu Data Mining, 2011 ISBN Vol. 13. Witold Pedrycz and Shyi-Ming Chen (Eds.) Granular Computing and Intelligent Systems, 2011 ISBN Vol. 14. George A. Anastassiou and Oktay Duman Towards Intelligent Modeling: Statistical Approximation Theory, 2011 ISBN Vol. 15. Antonino Freno and Edmondo Trentin Hybrid Random Fields, 2011 ISBN Vol. 16. Alexiei Dingli Knowledge Annotation: Making Implicit Knowledge Explicit, 2011 ISBN Vol. 17. Crina Grosan and Ajith Abraham Intelligent Systems, 2011 ISBN Vol. 18. Achim Zielesny From Curve Fitting to Machine Learning,2011 ISBN Vol. 19. George A. Anastassiou Intelligent Systems: Approximation by Artificial Neural Networks, 2011 ISBN Vol. 20. Lech Polkowski Approximate Reasoning by Parts, 2011 ISBN Vol. 21. Igor Chikalov Average Time Complexity of Decision Trees, 2011 ISBN Vol. 22. Przemys law Różewski, Emma Kusztina, Ryszard Tadeusiewicz, and Oleg Zaikin Intelligent Open Learning Systems, 2011 ISBN Vol. 23. Dawn E. Holmes and Lakhmi C. Jain (Eds.) Data Mining: Foundations and Intelligent Paradigms, 2012 ISBN Vol. 24. Dawn E. Holmes and Lakhmi C. Jain (Eds.) Data Mining: Foundations and Intelligent Paradigms, 2012 ISBN Vol. 25. Dawn E. Holmes and Lakhmi C. Jain (Eds.) Data Mining: Foundations and Intelligent Paradigms, 2012 ISBN

3 Dawn E. Holmes and Lakhmi C. Jain (Eds.) Data Mining: Foundations and Intelligent Paradigms Volume 3: Medical, Health, Social, Biological and other Applications 123

4 Prof.DawnE.Holmes Department of Statistics and Applied Probability University of California, Santa Barbara, CA USA Prof.LakhmiC.Jain Professor of Knowledge-Based Engineering University of South Australia Adelaide Mawson Lakes, SA 5095 Australia ISBN e-isbn DOI / Intelligent Systems Reference Library ISSN Library of Congress Control Number: c 2012 Springer-Verlag Berlin Heidelberg This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Typeset & Cover Design: Scientific Publishing Services Pvt. Ltd., Chennai, India. Printed on acid-free paper springer.com

5 Preface There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled DATA MINING: Foundations and Intelligent Paradigms: Volume 3: Medical, Health, Social, Biological and other Applications we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field. The term data mining was introduced in the 1990 s to describe an emerging field based on classical statistics, artificial intelligence and machine learning. By combining techniques from these areas, and developing new ones researchers are able to innovatively analyze large datasets productively. Patterns found in these datasets are subsequently analyzed with a view to acquiring new knowledge. These techniques have been applied in a broad range of medical, health, social and biological areas. In compiling this volume we have sought to present innovative research from prestigious contributors in the field of data mining. Each chapter is self-contained and is described briefly in Chapter 1. This book will prove valuable to theoreticians as well as application scientists/engineers in the area of Data Mining. Postgraduate students will also find this a useful sourcebook since it shows the direction of current research. We have been fortunate in attracting top class researchers as contributors and wish to offer our thanks for their support in this project. We also acknowledge the expertise and time of the reviewers. Finally, we also wish to thank Springer for their support. Dr. Dawn E. Holmes University of California Santa Barbara, USA Dr. Lakhmi C. Jain University of South Australia Adelaide, Australia

6 Contents Chapter 1 Advances in Intelligent Data Mining... 1 Dawn E. Holmes, Jeffrey W. Tweedale, Lakhmi C. Jain 1 Introduction Medical Influences Health Influences Social Influences InformationDiscovery On-LineCommunities Biological Influences BiologicalNetworks EstimationsinGeneExpression ChaptersIncludedintheBook Conclusion... 6 References... 6 Chapter 2 Temporal Pattern Mining for Medical Applications... 9 Giulia Bruno, Paolo Garza 1 Introduction TypesofTemporalDatainMedicalDomain Definitions TemporalPatternMining Algorithms Temporal Pattern Mining from a Set of Sequences Temporal Pattern Mining from a Single Sequence MedicalApplications Conclusions References Chapter 3 BioKeySpotter: An Unsupervised Keyphrase Extraction Technique in the Biomedical Full-Text Collection Min Song, Prat Tanapaisankit 1 Introduction... 19

7 VIII Contents 2 Backgrounds and Related Work TheProposedApproach Evaluation Dataset ComparisonAlgorithms ExperimentalResults Conclusion References Chapter 4 Mining Health Claims Data for Assessing Patient Risk Ian Duncan 1 WhatIsHealthRisk? TraditionalModelsforAssessingHealthRisk RiskFactor-BasedRiskModels DataSources EnrollmentData ClaimsandCodingSystems InterpretationofClaimsCodes ClinicalIdentificationAlgorithms Sensitivity-SpecificityTrade-Off ConstructinganIdentificationAlgorithm SourcesofAlgorithms ConstructionandUseofGrouperModels DrugGrouperModels Drug-BasedRiskAdjustmentModels SummaryandConclusions References Chapter 5 Mining Biological Networks for Similar Patterns Ferhat Ay, Günhan Gülsoy, Tamer Kahveci 1 Introduction Metabolic Network Alignment with One-to-One Mappings Model ProblemFormulation PairwiseSimilarityofEntities SimilarityofTopologies CombiningHomologyandTopology ExtractingtheMappingofEntities SimilarityScoreofNetworks ComplexityAnalysis Metabolic Network Alignment with One-to-Many Mappings Homological Similarity of Subnetworks Topological Similarity of Subnetworks

8 Contents IX 3.3 CombiningHomologyandTopology Extracting Subnetwork Mappings SignificanceofNetworkAlignment IdentificationofAlternativeEntities Identification of Alternative Subnetworks One-to-Many Mappings within and across Major Clades Summary FurtherReading References Chapter 6 Estimation of Distribution Algorithms in Gene Expression Data Analysis Elham Salehi, Robin Gras 1 Introduction EstimationofDistributionofAlgorithms ModelBuildinginEDA Notation ModelswithIndependentVariables ModelswithPairWiseDependencies ModelswithMultipleDependencies Application of EDA in Gene Expression Data Analysis State-of-Art of the Application of EDAs in Gene ExpressionDataAnalysis Conclusion References Chapter 7 Gene Function Prediction and Functional Network: The Role of Gene Ontology Erliang Zeng, Chris Ding, Kalai Mathee, Lisa Schneper, Giri Narasimhan 1 Introduction GeneFunctionPrediction FunctionalGeneNetworkGeneration RelatedWorkandLimitations GO-BasedGeneSimilarityMeasures Estimating Support for PPI Data with Applications to FunctionPrediction MixtureModelofPPIData DataSets FunctionPrediction EvaluatingtheFunctionPrediction ExperimentalResults Discussion

9 X Contents 4 A Functional Network of Yeast Genes Using Gene Ontology Information DataSets ConstructingaFunctionalGeneNetwork UsingSemanticSimilarity(SS) Evaluating the Functional Gene Network ExperimentalResults Discussion Conclusions References Chapter 8 Mining Multiple Biological Data for Reconstructing Signal Transduction Networks Thanh-Phuong Nguyen, Tu-Bao Ho 1 Introduction Background SignalTransductionNetwork Protein-ProteinInteraction Constructing Signal Transduction Networks Using Multiple Data RelatedWork MaterialsandMethods Clustering and Protein-Protein Interaction Networks Evaluation SomeResultsofYeastSTNReconstruction Outlook Summary References Chapter 9 Mining Epistatic Interactions from High-Dimensional Data Sets Xia Jiang, Shyam Visweswaran, Richard E. Neapolitan 1 Introduction Background Epistasis Detecting Epistasis High-DimensionalData Sets BarrierstoLearningEpistasis MDR BayesianNetworks DiscoveringEpistasisUsingBayesianNetworks A Bayesian Network Model for Epistatic Interactions TheBNMBLScore

10 Contents XI 3.3 Experiments EfficientSearch Experiments Discussion,Limitations,andFutureResearch References Chapter 10 Knowledge Discovery in Adversarial Settings D.B. Skillicorn 1 Introduction Characteristics of Adversarial Modelling TechnicalImplications Conclusion References Chapter 11 Analysis and Mining of Online Communities of Internet Forum Users Miko laj Morzy 1 Introduction WhatIsWeb2.0? New Forms of Participation Push or Pull? Internet Forums as New Forms of Conversation Social-DrivenData WhatAreSocial-DrivenData? DatafromInternetForums InternetForums CrawlingInternetForums StatisticalAnalysis IndexAnalysis NetworkAnalysis RelatedWork Conclusions References Chapter 12 Data Mining for Information Literacy Bettina Berendt 1 Introduction Background InformationLiteracy CriticalLiteracy EducationalDataMining Towards Critical Data Literacy: A Frame for Analysis and Design

11 XII Contents 3.1 AFrameofAnalysis:TechniqueandObject On the Chances of Achieving Critical Data Literacy: Principles of Successful Learning as Description Criteria Examples: Tools and Other Approaches Supporting Data MiningforInformationLiteracy Analysing Data: Do-It-Yourself Statistics Visualization Analysing Language: Viewpoints and Bias in Media Reporting Analysing Data Mining: Building, Comparing and Re-using Own and Others Conceptualizations of a Domain Analysing Actions: Feedback and Awareness Tools Analysing Actions: Role Reversals in Data Collection andanalysis SummaryandConclusions References Chapter 13 Rule Extraction from Neural Networks and Support Vector Machines for Credit Scoring Rudy Setiono, Bart Baesens, David Martens 1 Introduction Re-RX: Recursive Rule Extraction from Neural Networks MultilayerPerceptron Finding Optimal Network Structure by Pruning RecursiveRuleExtraction ApplyingRe-RXforCreditScoring ALBA: Rule Extraction from Support Vector Machines Support Vector Machine ALBA: Active Learning Based Approach to SVM Rule Extraction ApplyingALBAforCreditScoring Conclusion References Chapter 14 Using Self-Organizing Map for Data Mining: A Synthesis with Accounting Applications Andriy Andreev, Argyris Argyrou 1 Introduction DataPre-processing TypesofVariables DistanceMetrics

12 Contents XIII 2.3 Rescaling Input Variables Self-OrganizingMap IntroductiontoSOM FormationofSOM PerformanceMetricsandClusterValidity ExtensionsofSOM Non-metricSpaces SOMforTemporalSequenceProcessing SOMforClusterAnalysis SOM for Visualizing High-Dimensional Data FinancialApplicationsofSOM CaseStudy:ClusteringAccountingDatabases DataDescription DataPre-processing Experiments ResultsPresentationandDiscussion References Chapter 15 Applying Data Mining Techniques to Assess Steel Plant Operation Conditions Khan Muhammad Badruddin, Isao Yagi, Takao Terano 1 Introduction BriefDescriptionofEAF PerformanceEvaluationCriteria InnovationsinElectricArcFurnaces DetailsoftheOperation Understanding SCIPs and Stages of a Heat ProblemDescription DataMiningProcess Data DataPreprocessing AttributePruning TheExperiments DataMining Techniques Results Discussion ConcludingRemarks References Author Index

13 Editors Dr. Dawn E. Holmes serves as Senior Lecturer in the Department of Statistics and Applied Probability and Senior Associate Dean in the Division of Undergraduate Education at UCSB. Her main research area, Bayesian Networks with Maximum Entropy, has resulted in numerous journal articles and conference presentations. Her other research interests include Machine Learning, Data Mining, Foundations of Bayesianism and Intuitionistic Mathematics. Dr. Holmes has co-edited, with Professor Lakhmi C. Jain, volumes Innovations in Bayesian Networks and Innovations in Machine Learning. Dr. Holmes teaches a broad range of courses, including SAS programming, Bayesian Networks and Data Mining. She was awarded the Distinguished Teaching Award by Academic Senate, UCSB in As well as being Associate Editor of the International Journal of Knowledge-Based and Intelligent Information Systems, Dr. Holmes reviews extensively and is on the editorial board of several journals, including the Journal of Neurocomputing. She serves as Program Scientific Committee Member for numerous conferences; including the International Conference on Artificial Intelligence and the International Conference on Machine Learning. In 2009 Dr. Holmes accepted an invitation to join Center for Research in Financial Mathematics and Statistics (CRFMS), UCSB. She was made a Senior Member of the IEEE in Professor Lakhmi C. Jain is a Director/Founder of the Knowledge-Based Intelligent Engineering Systems (KES) Centre, located in the University of South Australia. He is a fellow of the Institution of Engineers Australia. His interests focus on the artificial intelligence paradigms and their applications in complex systems, artscience fusion, e-education, e-healthcare, unmanned air vehicles and intelligent agents.