Evolutionary Algorithms for Data Mining

Freitas, Alex A. (2005) Evolutionary Algorithms for Data Mining. In: Maimon, Oded and Rokach, Lior, eds. Data Mining and Knowledge Discovery Handbook. Springer, pp. 435-467. ISBN 0-387-24435-2. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Evolutionary Algorithms (EAs) are stochastic search algorithms inspired by the process of Darwinian evolution. The motivation for applying EAs to Data Mining is that they are robust, adaptive search techniques that perform a global search in the solution space. This chapter reviews mainly two kinds of EAs, viz. Genetic Algorithms (GAs) and Genetic Programming (GP), and discusses how EAs can be applied to several Data Mining tasks, namely: discovery of classification rules, clustering, attribute selection and attribute construction. It also discusses the basic idea of Multi-Objective EAs, based on the concept of Pareto dominance, which also has applications in Data Mining.

Item Type: Book section
Uncontrolled keywords: evolutionary algorithms, data mining, classification
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:03
Last Modified: 20 May 2014 09:01
Resource URI: https://kar.kent.ac.uk/id/eprint/14373 (The current URI for this page, for reference purposes)
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