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Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques

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Table of Contents

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    Book Overview
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    Chapter 1 A Common Logic Approach to Data Mining and Pattern Recognition
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    Chapter 2 The One Clause at a Time (OCAT) Approach to Data Mining and Knowledge Discovery
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    Chapter 3 An Incremental Learning Algorithm for Inferring Logical Rules from Examples in the Framework of the Common Reasoning Process
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    Chapter 4 Discovering Rules That Govern Monotone Phenomena
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    Chapter 5 Learning Logic Formulas and Related Error Distributions
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    Chapter 6 Feature Selection for Data Mining
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    Chapter 7 Transformation of Rational Data and Set Data to Logic Data
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    Chapter 8 Data Farming: Concepts and Methods
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    Chapter 9 Rule Induction Through Discrete Support Vector Decision Trees
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    Chapter 10 Multi-Attribute Decision Trees and Decision Rules
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    Chapter 11 Knowledge Acquisition and Uncertainty in Fault Diagnosis: A Rough Sets Perspective
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    Chapter 12 Discovering Knowledge Nuggets with a Genetic Algorithm
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    Chapter 13 Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques
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    Chapter 14 Fuzzy Logic in Discovering Association Rules: An Overview
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    Chapter 15 Mining Human Interpretable Knowledge with Fuzzy Modeling Methods: An Overview
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    Chapter 16 Data Mining from Multimedia Patient Records
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    Chapter 17 Learning to Find Context Based Spelling Errors
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    Chapter 18 Induction and Inference with Fuzzy Rules for Textual Information Retrieval
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    Chapter 19 Statistical Rule Induction in the Presence of Prior Information: The Bayesian Record Linkage Problem
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    Chapter 20 Some Future Trends in Data Mining
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