Clustering of Microarray data via clique partitioning

被引：30

作者：

Kochenberger, G ^{[1
]}

Glover, F

Alidaee, B

Wang, HB

机构：

[1] Univ Colorado, Sch Business, Denver, CO 80202 USA

[2] Univ Colorado, Leeds Sch Business, Boulder, CO 80309 USA

[3] Univ Mississippi, Sch Business, University, MS 38677 USA

[4] Texas A&M Int Univ, Sch Business, Laredo, TX 78041 USA

来源：

JOURNAL OF COMBINATORIAL OPTIMIZATION | 2005年 / 10卷 / 01期

关键词：

clustering; clique partitioning; metaheuristics;

D O I：

10.1007/s10878-005-1861-1

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Microarrays are repositories of gene expression data that hold tremendous potential for new understanding, leading to advances in functional genomics and molecular biology. Cluster analysis (CA) is an early step in the exploration of such data that is useful for purposes of data reduction, exposing hidden patterns, and the generation of hypotheses regarding the relationship between genes and phenotypes. In this paper we present a new model for the clique partitioning problem and illustrate how it can be used to perform cluster analysis in this setting.

引用

页码：77 / 92

页数：16

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