Evolutionary algorithms for clustering gene-expression data

被引：45
|
作者：
Hruschka, ER
de Castro, LN
Campello, RJGB
机构：
来源：
FOURTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS | 2004年
关键词：
D O I：
10.1109/ICDM.2004.10073
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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 EA C is a promising tool for clustering gene-expression data.
引用
收藏
页码：403 / 406
页数：4
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