Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010088337 | QA9.64 C69 2005 | Open Access Book | Book | Searching... |
Searching... | 30000010075499 | QA9.64 C69 2005 | Open Access Book | Book | Searching... |
Searching... | 30000010088336 | QA9.64 C69 2005 | Open Access Book | Book | Searching... |
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Summary
Summary
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.
Author Notes
Earl Cox is the founder and president of Scianta Intelligence, a next-generation machine intelligence and knowledge exploration company
Table of Contents
| Preface |
| Acknowledgements |
| Introduction |
| Part 1 Concepts and Issues |
| Chapter 1 Foundations and Ideas |
| Chapter 2 Principal Model Types |
| Chapter 3 Approaches to Model Building |
| Part 2 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 3 Evolutionary Strategies |
| Chapter 9 Fundamental Concepts of Genetic Algorithms |
| Chapter 10 Genetic Resource Scheduling Optimization |
| Chapter 11 Genetic Tuning of Fuzzy Models |
