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Fuzzy modeling tools for data mining and knowledge discovery

By: Cox, Earl (Scianta Intelligence, LLC, Chapel Hill, NC)

Part of the The Morgan Kaufmann Series in Data Management Systems series
0121942759 / 9780121942755
Laminated
005.7'41
18/01/2005
Usually dispatched within 4 weeks
300p.
academic/professional/technical  Learn More
* Written to provide analysts, engineers, and managers with the background and specific instruction needed to develop and implement more effective data mining systems.<p/>* Helps you to understand the trade-offs implicit in various models and model architectures.<p/>* Provides extensive coverage of fuzzy SQL querying, fuzzy clustering, and fuzzy rule induction.<p/>* 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.<p/>* In an extended example,

"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 will 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 do not 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.

This book is written to provide analysts, engineers, and managers with the background and specific instruction needed to develop and implement more effective data mining systems.It 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; and, 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, it applies evolutionary programming techniques to solve a complicated scheduling problem.

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

BIC:

UNC Data capture & analysis, UYQ Artificial intelligence

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RRP £50.99
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