Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques

This book will give the reader a perspective into the core theory and practice of data mining and knowledge discovery (DM&KD). Its chapters combine many theoretical foundations for various DM&KD methods, and they present a rich array of examples-many of which are drawn from real-life applications. Most of the theoretical developments discussed are accompanied by an extensive empirical analysis, which should give the reader both a deep theoretical and practical insight into the subjects covered.

The book presents the combined research experiences of its 40 authors gathered during a long search in gleaning new knowledge from data. The last page of each chapter has a brief biographical statement of its contributors, who are world-renowned experts.

Audience

The intended audience for this book includes graduate students studying data mining who have some background in mathematical logic and discrete optimization, as well as researchers and practitioners in the same area.

Zusammenfassung

2. Some Background Information 49 3. Definitions and Terminology 52 4. The One Clause at a Time (OCAT) Approach 54 4. 1 Data Binarization 54 4. 2 The One Clause at a Time (OCAT) Concept 58 4. 3 A Branch-and-Bound Approach for Inferring Clauses 59 4. 4 Inference of the Clauses for the Illustrative Example 62 4. 5 A Polynomial Time Heuristic for Inferring Clauses 65 5. A Guided Learning Approach 70 6. The Rejectability Graph of Two Collections of Examples 72 6. 1 The Definition of the Rej ectability Graph 72 6. 2 Properties of the Rejectability Graph 74 6. 3 On the Minimum Clique Cover of the Rej ectability Graph 76 7. Problem Decomposition 77 7. 1 Connected Components 77 7. 2 Clique Cover 78 8. An Example of Using the Rejectability Graph 79 9. Conclusions 82 References 83 Author's Biographical Statement 87 Chapter 3 AN INCREMENTAL LEARNING ALGORITHM FOR INFERRING LOGICAL RULES FROM EXAMPLES IN THE FRAMEWORK OF THE COMMON REASONING PROCESS, by X. Naidenova 89 1. Introduction 90 2. A Model of Rule-Based Logical Inference 96 2. 1 Rules Acquired from Experts or Rules of the First Type 97 2. 2 Structure of the Knowledge Base 98 2. 3 Reasoning Operations for Using Logical Rules of the First Type 100 2. 4 An Example of the Reasoning Process 102 3. Inductive Inference of Implicative Rules From Examples 103 3.

Inhalt

A Common Logic Approach to Data Mining and Pattern Recognition.- The One Clause at a Time (OCAT) Approach to Data Mining and Knowledge Discovery.- An Incremental Learning Algorithm for Inferring Logical Rules from Examples in the Framework of the Common Reasoning Process.- Discovering Rules That Govern Monotone Phenomena.- Learning Logic Formulas and Related Error Distributions.- Feature Selection for Data Mining.- Transformation of Rational Data and Set Data to Logic Data.- Data Farming: Concepts and Methods.- Rule Induction Through Discrete Support Vector Decision Trees.- Multi-Attribute Decision Trees and Decision Rules.- Knowledge Acquisition and Uncertainty in Fault Diagnosis: A Rough Sets Perspective.- Discovering Knowledge Nuggets with a Genetic Algorithm.- Diversity Mechanisms in Pitt-Style Evolutionary Classifier Systems.- Fuzzy Logic in Discovering Association Rules: An Overview.- Mining Human Interpretable Knowledge with Fuzzy Modeling Methods: An Overview.- Data Mining from Multimedia Patient Records.- Learning to Find Context Based Spelling Errors.- Induction and Inference with Fuzzy Rules for Textual Information Retrieval.- Statistical Rule Induction in the Presence of Prior Information: The Bayesian Record Linkage Problem.- Some Future Trends in Data Mining.