This book outlines the core theory and practice of data mining and knowledge discovery (DM & KD) examining theoretical foundations for various methods, and presenting an array of examples, many drawn from real-life applications. Most theoretical developments are accompanied by extensive empirical analysis, offering a deep insight into both theoretical and practical aspects of the subject. The book presents the combined research experiences of 40 expert contributors of world renown.
List of Figures List of Tables Foreword by Panos M. Pardalos Preface Acknowledgements Chapter 1. A COMMON LOGIC APPROACH TO DATA MINING AND PATTERN RECOGNITION, by A. Zakrevskij Chapter 2. THE ONE CLAUSE AT A TIME (OCAT) APPROACH TO DATA MINING AND KNOWLEDGE DISCOVERY, by E. Triantaphyllou Chapter 3. AN INCREMENTAL LEARNING ALGORITHM FOR INFERRING LOGICAL RULES FROM EXAMPLES IN THE FRAMEWORK OF THE COMMON REASONING PROCESS, by X. Naidenova Chapter 4. DISCOVERING RULES THAT GOVERN MONOTONE PHENOMENA, by V.I. Torvik and E. Triantaphyllou Chapter 5. LEARNING LOGIC FORMULAS AND RELATED ERROR DISTRIBUTIONS, by G. Felici, F. Sun, and K. Truemper Chapter 6. FEATURE SELECTION FOR DATA MINING by V. de Angelis, G. Felici, and G. Mancinelli Chapter 7. TRANSFORMATION OF RATIONAL AND SET DATA TO LOGIC DATA, by S. Bartnikowski, M. Granberry, J. Mugan, and K. Truemper Chapter 8. DATA FARMING: CONCEPTS AND METHODS, by A. Kusiak Chapter 9. RULE INDUCTION THROUGH DISCRETE SUPPORT VECTOR DECISION TREES, by C. Orsenigo and C. Vercellis Chapter 10. MULTI-ATTRIBUTE DECISION TREES AND DECISION RULES, by J.-Y. Lee and S. Olafsson Chapter 11. KNOWLEDGE ACQUISITION AND UNCERTAINTY IN FAULT DIAGNOSIS: A ROUGH SETS PERSPECTIVE, by L.-Y. Zhai, L.-P. Khoo, and S.-C. Fok Chapter 12. DISCOVERING KNOWLEDGE NUGGETS WITH A GENETIC ALGORITHM, by E. Noda and A.A. Freitas Chapter 13. DIVERSITY MECHANISMS IN PITT-STYLE EVOLUTIONARY CLASSIFIER SYSTEMS, by M. Kirley, H.A. Abbass and R.I. McKay Chapter 14. FUZZY LOGIC IN DISCOVERING ASSOCIATION RULES: AN OVERVIEW, by G. Chen, Q. Wei and E.E. Kerre Chapter 15. MINING HUMAN INTERPRETABLE KNOWLEDGE WITH FUZZY MODELING METHODS: AN OVERVIEW, by T.W. Liao Chapter 16. DATA MINING FROM MULTIMEDIA PATIENT RECORDS, by A.S. Elmaghraby, M.M. Kantardzic, and M.P. Wachowiak Chapter 17. LEARNING TO FIND CONTEXT BASED SPELLING ERRORS, by H. Al-Mubaid and K. Truemper Chapter 18. INDUCTION AND INFERENCE WITH FUZZY RULES FOR TEXTUAL INFORMATION RETRIEVAL, by J. Chen, D.H. Kraft, M.J. Martin-Bautista, and M.–A. Vila Chapter 19. STATISTICAL RULE INDUCTION IN THE PRESENCE OF PRIOR INFORMATION: THE BAYESIAN RECORD LINKAGE PROBLEM, by D.H. Judson Chapter 20. FUTURE TRENDS IN SOME DATA MINING AREAS, by X. Wang, P. Zhu, G. Felici, and E. Triantaphyllou Subject Index Author Index Contributor Index About the Editors
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This book outlines the core theory and practice of data mining and knowledge discovery (DM & KD) examining theoretical foundations for various methods, and presenting an array of examples, many drawn from real-life applications. Most theoretical developments are accompanied by extensive empirical analysis, offering a deep insight into both theoretical and practical aspects of the subject. The book presents the combined research experiences of 40 expert contributors of world renown.
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List of Figures List of Tables Foreword by Panos M. Pardalos Preface Acknowledgements Chapter 1. A COMMON LOGIC APPROACH TO DATA MINING AND PATTERN RECOGNITION, by A. Zakrevskij Chapter 2. THE ONE CLAUSE AT A TIME (OCAT) APPROACH TO DATA MINING AND KNOWLEDGE DISCOVERY, by E. Triantaphyllou Chapter 3. AN INCREMENTAL LEARNING ALGORITHM FOR INFERRING LOGICAL RULES FROM EXAMPLES IN THE FRAMEWORK OF THE COMMON REASONING PROCESS, by X. Naidenova Chapter 4. DISCOVERING RULES THAT GOVERN MONOTONE PHENOMENA, by V.I. Torvik and E. Triantaphyllou Chapter 5. LEARNING LOGIC FORMULAS AND RELATED ERROR DISTRIBUTIONS, by G. Felici, F. Sun, and K. Truemper Chapter 6. FEATURE SELECTION FOR DATA MINING by V. de Angelis, G. Felici, and G. Mancinelli Chapter 7. TRANSFORMATION OF RATIONAL AND SET DATA TO LOGIC DATA, by S. Bartnikowski, M. Granberry, J. Mugan, and K. Truemper Chapter 8. DATA FARMING: CONCEPTS AND METHODS, by A. Kusiak Chapter 9. RULE INDUCTION THROUGH DISCRETE SUPPORT VECTOR DECISION TREES, by C. Orsenigo and C. Vercellis Chapter 10. MULTI-ATTRIBUTE DECISION TREES AND DECISION RULES, by J.-Y. Lee and S. Olafsson Chapter 11. KNOWLEDGE ACQUISITION AND UNCERTAINTY IN FAULT DIAGNOSIS: A ROUGH SETS PERSPECTIVE, by L.-Y. Zhai, L.-P. Khoo, and S.-C. Fok Chapter 12. DISCOVERING KNOWLEDGE NUGGETS WITH A GENETIC ALGORITHM, by E. Noda and A.A. Freitas Chapter 13. DIVERSITY MECHANISMS IN PITT-STYLE EVOLUTIONARY CLASSIFIER SYSTEMS, by M. Kirley, H.A. Abbass and R.I. McKay Chapter 14. FUZZY LOGIC IN DISCOVERING ASSOCIATION RULES: AN OVERVIEW, by G. Chen, Q. Wei and E.E. Kerre Chapter 15. MINING HUMAN INTERPRETABLE KNOWLEDGE WITH FUZZY MODELING METHODS: AN OVERVIEW, by T.W. Liao Chapter 16. DATA MINING FROM MULTIMEDIA PATIENT RECORDS, by A.S. Elmaghraby, M.M. Kantardzic, and M.P. Wachowiak Chapter 17. LEARNING TO FIND CONTEXT BASED SPELLING ERRORS, by H. Al-Mubaid and K. Truemper Chapter 18. INDUCTION AND INFERENCE WITH FUZZY RULES FOR TEXTUAL INFORMATION RETRIEVAL, by J. Chen, D.H. Kraft, M.J. Martin-Bautista, and M.–A. Vila Chapter 19. STATISTICAL RULE INDUCTION IN THE PRESENCE OF PRIOR INFORMATION: THE BAYESIAN RECORD LINKAGE PROBLEM, by D.H. Judson Chapter 20. FUTURE TRENDS IN SOME DATA MINING AREAS, by X. Wang, P. Zhu, G. Felici, and E. Triantaphyllou Subject Index Author Index Contributor Index About the Editors