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Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration

by Earl Cox

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Fuzzy systems and evolutionary methods for applied data mining

$86.62 USD
$86.62 USD $86.62 USD

Paperback New English

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AlbakiReads Editorial

Fuzzy systems and evolutionary methods for applied data mining

A practical guide to building data-mining models with fuzzy systems, genetic algorithms, and evolutionary programming. Cox introduces the underlying concepts for newcomers, then connects them to the work of analysts, engineers, and managers developing models in business and government contexts. The material centers on using fuzzy approaches to represent and work with varied data, while evolutionary methods are presented as tools for designing and tuning those systems. Diverse examples accompany the instruction, including an extended scheduling-system case built through evolutionary strategies. The emphasis is on methods that can be carried into project work, not on data mining as a purely theoretical subject.

Good fit for readers who enjoy

  • Analysts and engineers developing applied data-mining models
  • Managers seeking an introduction to fuzzy modeling and genetic algorithms
  • Readers interested in evolutionary approaches to scheduling and model design

Themes

Data miningFuzzy modelingGenetic algorithmsEvolutionary programmingScheduling systems

Based on publisher information and book metadata.

Book overview

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.

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