DocumentCode
2850947
Title
Evolutionary algorithms for clustering gene-expression data
Author
Hruschka, Eduardo R. ; de Castro, Leandro N. ; Campello, Ricardo J G B
Author_Institution
Univ. Catolica de Santos, Brazil
fYear
2004
fDate
1-4 Nov. 2004
Firstpage
403
Lastpage
406
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.
Keywords
biology; genetic algorithms; pattern clustering; bioinformatics; clustering genetic algorithm; evolutionary algorithms; gene-expression data clustering; Algorithm design and analysis; Bioinformatics; Clustering algorithms; Design optimization; Encoding; Evolutionary computation; Gene expression; Genetic algorithms; Partitioning algorithms; Pattern recognition;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Mining, 2004. ICDM '04. Fourth IEEE International Conference on
Print_ISBN
0-7695-2142-8
Type
conf
DOI
10.1109/ICDM.2004.10073
Filename
1410321
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