Evolutionary Algorithms for Clustering Gene-Expression Data

I
ICDM
2004
Fourth IEEE International Conference on Data Mining (ICDM'04)
Abstract - Evolutionary Algorithms for Clustering Gene-Expression Data

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	Similar Articles Articles by Eduardo R. Hruschka Articles by Leandro N. de Castro Articles by Ricardo J. G. B. Campello

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

Brighton, United Kingdom

November 01-November 04

ISBN: 0-7695-2142-8

	ASCII Text	x
	Eduardo R. Hruschka, Leandro N. de Castro, Ricardo J. G. B. Campello, "Evolutionary Algorithms for Clustering Gene-Expression Data," Data Mining, IEEE International Conference on, pp. 403-406, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004.

	BibTex	x
	@article{ 10.1109/ICDM.2004.10073, author = {Eduardo R. Hruschka and Leandro N. de Castro and Ricardo J. G. B. Campello}, title = {Evolutionary Algorithms for Clustering Gene-Expression Data}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2004}, isbn = {0-7695-2142-8}, pages = {403-406}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10073}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - Evolutionary Algorithms for Clustering Gene-Expression Data SN - 0-7695-2142-8 SP403 EP406 A1 - Eduardo R. Hruschka, A1 - Leandro N. de Castro, A1 - Ricardo J. G. B. Campello, PY - 2004 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

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)

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10073

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.

Citation:

Eduardo R. Hruschka, Leandro N. de Castro, Ricardo J. G. B. Campello, "Evolutionary Algorithms for Clustering Gene-Expression Data," icdm, pp.403-406, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004

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