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
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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Including with reference and index