Gene selection by cooperative competition clustering

S. Pei, D.S. Huang, Kang Li, George Irwin

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review


Clustering analysis of data from DNA microarray hybridization studies is an essential task for identifying biologically relevant groups of genes. Attribute cluster algorithm (ACA) has provided an attractive way to group and select meaningful genes. However, ACA needs much prior knowledge about the genes to set the number of clusters. In practical applications, if the number of clusters is misspecified, the performance of the ACA will deteriorate rapidly. In fact, it is a very demanding to do that because of our little knowledge. We propose the Cooperative Competition Cluster Algorithm (CCCA) in this paper. In the algorithm, we assume that both cooperation and competition exist simultaneously between clusters in the process of clustering. By using this principle of Cooperative Competition, the number of clusters can be found in the process of clustering. Experimental results on a synthetic and gene expression data are demonstrated. The results show that CCCA can choose the number of clusters automatically and get excellent performance with respect to other competing methods.
Original languageEnglish
Title of host publication Computational Intelligence and Bioinformatics
Number of pages11
Publication statusPublished - Aug 2006
EventInternational Conference on Intelligent Computing - Kunming, China
Duration: 01 Aug 200601 Aug 2006

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


ConferenceInternational Conference on Intelligent Computing
Abbreviated titleICIC


Dive into the research topics of 'Gene selection by cooperative competition clustering'. Together they form a unique fingerprint.

Cite this