Evolutionary Algorithms for Clustering Gene-Expression Data

Authors:
Eduardo R. Hruschka;Leandro N. de Castro;Ricardo J. G. B. Campello
Affiliations:
Universidade Católica de Santos (UniSantos);Universidade Católica de Santos (UniSantos);Universidade Católica de Santos (UniSantos)
Venue:
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Year:
2004

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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.