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Clustering of Microarray data via Clique Partitioning

Author

Listed:
  • 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)

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.

Suggested Citation

  • Gary Kochenberger & Fred Glover & Bahram Alidaee & Haibo Wang, 2005. "Clustering of Microarray data via Clique Partitioning," Journal of Combinatorial Optimization, Springer, vol. 10(1), pages 77-92, August.
    • Handle: RePEc:spr:jcomop:v:10:y:2005:i:1:d:10.1007_s10878-005-1861-1
    DOI: 10.1007/s10878-005-1861-1
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    References listed on IDEAS

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    1. Ulrich Dorndorf & Erwin Pesch, 1994. "Fast Clustering Algorithms," INFORMS Journal on Computing, INFORMS, vol. 6(2), pages 141-153, May.
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    Cited by:

    1. Fred Glover & Gary Kochenberger & Rick Hennig & Yu Du, 2022. "Quantum bridge analytics I: a tutorial on formulating and using QUBO models," Annals of Operations Research, Springer, vol. 314(1), pages 141-183, July.
    2. Jovanovic, Raka & Sanfilippo, Antonio P. & Voß, Stefan, 2023. "Fixed set search applied to the clique partitioning problem," European Journal of Operational Research, Elsevier, vol. 309(1), pages 65-81.
    3. Larisa Komosko & Mikhail Batsyn & Pablo San Segundo & Panos M. Pardalos, 2016. "A fast greedy sequential heuristic for the vertex colouring problem based on bitwise operations," Journal of Combinatorial Optimization, Springer, vol. 31(4), pages 1665-1677, May.
    4. Noriyoshi Sukegawa & Yoshitsugu Yamamoto & Liyuan Zhang, 2013. "Lagrangian relaxation and pegging test for the clique partitioning problem," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(4), pages 363-391, December.
    5. Andrews, Rick L. & Brusco, Michael J. & Currim, Imran S., 2010. "Amalgamation of partitions from multiple segmentation bases: A comparison of non-model-based and model-based methods," European Journal of Operational Research, Elsevier, vol. 201(2), pages 608-618, March.
    6. Oleksandra Yezerska & Foad Mahdavi Pajouh & Alexander Veremyev & Sergiy Butenko, 2019. "Exact algorithms for the minimum s-club partitioning problem," Annals of Operations Research, Springer, vol. 276(1), pages 267-291, May.

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