Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

Produktdetails

• Verlag: Springer US
• Erscheinungstermin: 10. September 2006
• Englisch
• ISBN-13: 9780387342962
• Artikelnr.: 37286211

Inhaltsangabe

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.

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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.