Clustering of Microarray data via Clique Partitioning

Gary Kochenberger (), Fred Glover (), Bahram Alidaee () and Haibo Wang ()
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Gary Kochenberger: University of Colorado at Denver
Fred Glover: University of Colorado at Boulder
Bahram Alidaee: University of Mississippi
Haibo Wang: Texas A&M International University

Journal of Combinatorial Optimization, 2005, vol. 10, issue 1, No 6, 77-92

Abstract: Abstract 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.

Keywords: clustering; clique partitioning; metaheuristics (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s10878-005-1861-1

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