Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration

Autor: Earl Cox

Limbă:

Legare: Carte broșată

Editura: Morgan Kaufmann Publishers

Disponibilitate: În depozitul extern

Expediem în 10-18 zile

380.24 lei

"Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration" is a handbook for analysts,...

Informații despre carte

Autor

Earl Cox

Limbă

engleză

Legare

Carte - Carte broșată

Publicat

2005

Pagini

540

EAN

9780121942755

ISBN

0121942759

Enbook ID

04859789

Editura

Morgan Kaufmann Publishers

Greutate

1110

Dimensiuni

184 x 259 x 29

Descriere completă

"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 systems, this work: 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; and, presents examples in C, C++, Java, and easy-to-understand pseudo-code. It also features an extensive online component, including sample code and a complete data mining workbench.

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Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration

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