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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. (doi:10.1007/b107408) (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) (KAR id:14373)

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. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1007/b107408

Abstract

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
DOI/Identification number: 10.1007/b107408
Uncontrolled keywords: evolutionary algorithms, data mining, classification
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:03 UTC
Last Modified: 16 Feb 2021 12:25 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/14373 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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