Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration

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.

Written to provide analysts, engineers, and managers with the background and specific instruction needed to develop and implement more effective data mining systemsHelps you to understand the trade-offs implicit in various models and model architecturesProvides extensive coverage of fuzzy SQL querying, fuzzy clustering, and fuzzy rule inductionLays 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 modelIn an extended example, applies evolutionary programming techniques to solve a complicated scheduling problemPresents examples in C, C++, Java, and easy-to-understand pseudo-codeExtensive online component, including sample code and a complete data mining workbench

Preface
Acknowledgements
Introduction

PART ONE – CONCEPTS AND ISSUES

Chapter 1. Foundations and Ideas
Chapter 2. Principal Model Types
Chapter 3. Approaches to Model Building

PART TWO – FUZZY SYSTEMS

Chapter 4. Fundamental Concepts of Fuzzy Logic
Chapter 5. Fundamental Concepts of Fuzzy Systems
Chapter 6. FuzzySQL and Intelligent Queries
Chapter 7. Fuzzy Clustering
Chapter 8. Fuzzy Rule Induction

PART THREE – EVOLUTIONARY STRATEGIES

Chapter 9. Fundamental Concepts of Genetic Algorithms
Chapter 10. Genetic Resource Scheduling Optimization
Chapter 11. Genetic Tuning of Fuzzy Models