Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration is a handbook for analysts, engineers, and managers involved in developing data mining models in business and government. As you’ll discover, fuzzy systems are extraordinarily valuable tools for representing and manipulating all kinds of data, and genetic algorithms and evolutionary programming techniques drawn from biology provide the most effective means for designing and tuning these systems. You don’t need a background in fuzzy modeling or genetic algorithms to benefit, for this book provides it, along with detailed instruction in methods that you can immediately put to work in your own projects. The author provides many diverse examples and also an extended example in which evolutionary strategies are used to create a complex scheduling system.

Key Features

* Written to provide analysts, engineers, and managers with the background and specific instruction needed to develop and implement more effective data mining systems. * Helps you to understand the trade-offs implicit in various models and model architectures. * Provides extensive coverage of fuzzy SQL querying, fuzzy clustering, and fuzzy rule induction. * Lays out a roadmap for exploring data, selecting model system measures, organizing adaptive feedback loops, selecting a model configuration, implementing a working model, and validating the final model. * In an extended example, applies evolutionary programming techniques to solve a complicated scheduling problem. * Presents examples in C, C++, Java, and easy-to-understand pseudo-code. * Extensive online component, including sample code and a complete data mining workbench.


Researchers and technicians in organizations with large databases.

Table of Contents

Preface Acknowledgements Introduction PART ONE – CONCEPTS AND ISSUES Chapter 1. FOUNDATIONS AND IDEAS 1.1 Enterprise Applications and Analysis Models 1.2 Distributed and Centralized Repositories 1.3 The Age of Distributed Knowledge 1.4 Information and Knowledge Discovery 1.5 Data Mining and Business Models 1.6 Fuzzy Systems for Business Process Models 1.7 Evolving Distributed Fuzzy Models 1.8 A Sample Case – Evolving a Model for Customer Segmentation Review Chapter 2. PRINCIPAL MODEL TYPES 2.1 Model and Event State Categorization 2.2 Model Type and Outcome Categorization Review Chapter 3. APPROACHES TO MODEL BUILDING 3.1 Ordinary Statistics. 3.2 Non-Parametric Statistics 3.3 Linear Regression In Statistical Models 3.4 Non-Linear Growth Curve Fitting 3.5 Cluster Analysis 3.6 Decision Trees and Classifiers 3.7 Neural Networks 3.8 Fuzzy SQL Systems 3.9 Rule Induction and Dynamic Fuzzy Models Review References PART TWO – FUZZY SYSTEMS Chapter 4. FUNDAMENTAL CONCEPTS OF FUZZY LOGIC 4.1 The Vocabulary of Fuzzy Logic 4.2 Boolean (Crisp) Sets – The Law of Bivalence 4.3 Fuzzy Sets Review Chapter 5. FUNDAMENTAL CONCEPTS OF FUZZY SYSTEMS 5.1 The Vocabulary of Fuzzy Systems 5.2 Fuzzy Rule-Based Systems – An Overview 5.3 Fuzzy Rules 5.4 Variable Decomposit


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© 2005
Morgan Kaufmann
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