Genetic clustering for data mining

M. C. Naldi, André C. P. L. F. Carvalho, R. J. G. B. Campello, E. R. Hruschka

Publikation: Kapitel i bog/rapport/konference-proceedingKapitel i bogForskningpeer review

Abstract

Genetic Algorithms (GAs) have been successfully applied to several complex data analysis problems in a wide range of domains, such as image processing, bioinformatics, and crude oil analysis. The need for organizing data into categories of similar objects has made the task of clustering increasingly important to those domains. In this chapter, the authors present a survey of the use of GAs for clustering applications. A variety of encoding (chromosome representation) approaches, fitness functions, and genetic operators are described, all of them customized to solve problems in such an application context.
OriginalsprogEngelsk
TitelSoft Computing for Knowledge Discovery and Data Mining
RedaktørerOded Maimon, Lior Rokach
ForlagSpringer
Publikationsdato2008
Sider113-132
ISBN (Trykt)978-0-387-69934-9
ISBN (Elektronisk)978-0-387-69935-6
DOI
StatusUdgivet - 2008
Udgivet eksterntJa

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