Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration

ebook The Morgan Kaufmann Series in Data Management Systems

By Earl Cox

cover image of Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration

Sign up to save your library

With an OverDrive account, you can save your favorite libraries for at-a-glance information about availability. Find out more about OverDrive accounts.

   Not today
Libby_app_icon.svg

Find this title in Libby, the library reading app by OverDrive.

app-store-button-en.svg play-store-badge-en.svg
LibbyDevices.png

Search for a digital library with this title

Title found at these libraries:

Loading...

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
  • Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration