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Clustering of Microarray data via Clique Partitioning

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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.

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Authors and Affiliations

  1. School of Business, University of Colorado at Denver, Denver

    Gary Kochenberger

  2. Leeds School of Business, University of Colorado at Boulder, Boulder

    Fred Glover

  3. School of Business, University of Mississippi, Mississippi

    Bahram Alidaee

  4. School of Business, Texas A&M International University, Texas

    Haibo Wang

Authors
  1. Gary Kochenberger
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  2. Fred Glover
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  3. Bahram Alidaee
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  4. Haibo Wang
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Corresponding author

Correspondence to Gary Kochenberger.

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Kochenberger, G., Glover, F., Alidaee, B. et al. Clustering of Microarray data via Clique Partitioning. J Comb Optim 10, 77–92 (2005). https://doi.org/10.1007/s10878-005-1861-1

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  • DOI: https://doi.org/10.1007/s10878-005-1861-1

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