Evolutionary Algorithms for Clustering Gene-Expression Data

Eduardo R. Hruschka; Leandro N. de Castro; Ricardo J. G. B. Campello

doi:10.1109/ICDM.2004.10073

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Fourth IEEE International Conference on Data Mining (ICDM'04)

Evolutionary Algorithms for Clustering Gene-Expression Data

Year: 2004, Pages: 403-406

DOI Bookmark: 10.1109/ICDM.2004.10073

Authors

Eduardo R. Hruschka, Universidade Cat?lica de Santos (UniSantos)
Leandro N. de Castro, Universidade Cat?lica de Santos (UniSantos)
Ricardo J. G. B. Campello, Universidade Cat?lica de Santos (UniSantos)

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.

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