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Bibliografická citace

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Amsterdam ; Boston : Elsevier/Morgan Kaufmann, c2005
1 online zdroj (xxi, 530 s.) : il.
Externí odkaz    Plný text PDF 
   * Návod pro vzdálený přístup 

ISBN 0080470599 (ebook)
ISBN 0121942759
ISBN 9780080470597 (ebook)
ISBN 9780121942755
The Morgan Kaufmann series in data management systems
Tištěná verze: Cox, Earl. Fuzzy modeling and genetic algorithms for data mining and exploration. Amsterdam ; Boston : Elsevier/Morgan Kaufmann, c2005 ISBN 0121942759 ISBN 9780121942755
Obsahuje bibliografické odkazy a rejstřík
Foundations and ideas -- Principal model types -- Approaches to model building -- Fundamental concepts of fuzzy logic -- Fundamental concepts of fuzzy systems -- Fuzzy SQL and intelligent queries -- Fuzzy clustering -- Fuzzy rule induction -- Fundamental concepts of genetic algorithms -- Genetic resource scheduling optimization -- Genetic tuning of fuzzy models
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 youll 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 dont 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.
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