Evolutionary algorithms for clustering gene-expression data

Eduardo R. Hruschka, Leandro N. De Castro, Ricardo J.G.B. Campello

Publikation: Kapitel i bog/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

Abstract

This work deals with the problem of automatically finding optimal partitions in bioinformatics datasets. We propose incremental improvements for a Clustering Genetic Algorithm (CGA), culminating in the Evolutionary Algorithm for Clustering (EAC). The CGA and its modified versions are evaluated in five gene-expression datasets, showing that the proposed EAC is a promising tool for clustering gene-expression data.

OriginalsprogEngelsk
TitelProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
RedaktørerR. Rastogi, K. Morik, M. Bramer, X. Wu
ForlagIEEE
Publikationsdato2004
Sider403-406
ISBN (Trykt)0769521428, 9780769521428
DOI
StatusUdgivet - 2004
Udgivet eksterntJa
BegivenhedProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004 - Brighton, Storbritannien
Varighed: 1. nov. 20044. nov. 2004

Konference

KonferenceProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
Land/OmrådeStorbritannien
ByBrighton
Periode01/11/200404/11/2004
SponsorIEEE Computer Society TCCI, IEEE Computer Society TCPAMI

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