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Evolutionary algorithms for clustering gene-expression data

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3 Author(s)
Hruschka, E.R. ; Univ. Catolica de Santos, Brazil ; de Castro, L.N. ; Campello, R.J.G.B.

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.

Published in:

Data Mining, 2004. ICDM '04. Fourth IEEE International Conference on

Date of Conference:

1-4 Nov. 2004

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