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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 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.
"...focuses on recently developed "fuzzy" techniques & how they can be applied to discern patterns buried deep within the data. Paired with a CD of Metus/KDS data mining workbench & more to use in the reader's own projects."
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