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A Novel Mixed Integer Linear Programming Model for Clustering Relational Networks

Author

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  • Harun Pirim

    (King Fahd University of Petroleum and Minerals)

  • Burak Eksioglu

    (Clemson University)

  • Fred W. Glover

    (University of Colorado)

Abstract

Integer programming models for clustering have applications in diverse fields addressing many problems such as market segmentation and location of facilities. Integer programming models are flexible in expressing objectives subject to some special constraints of the clustering problem. They are also important for guiding clustering algorithms that are capable of handling high-dimensional data. Here, we present a novel mixed integer linear programming model especially for clustering relational networks, which have important applications in social sciences and bioinformatics. Our model is applied to several social network data sets to demonstrate its ability to detect natural network structures.

Suggested Citation

  • Harun Pirim & Burak Eksioglu & Fred W. Glover, 2018. "A Novel Mixed Integer Linear Programming Model for Clustering Relational Networks," Journal of Optimization Theory and Applications, Springer, vol. 176(2), pages 492-508, February.
    • Handle: RePEc:spr:joptap:v:176:y:2018:i:2:d:10.1007_s10957-017-1213-1
    DOI: 10.1007/s10957-017-1213-1
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    References listed on IDEAS

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    1. Saglam, Burcu & Salman, F. Sibel & Sayin, Serpil & Turkay, Metin, 2006. "A mixed-integer programming approach to the clustering problem with an application in customer segmentation," European Journal of Operational Research, Elsevier, vol. 173(3), pages 866-879, September.
    2. Fred W. Glover & Gary Kochenberger, 2006. "New Optimization Models For Data Mining," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 5(04), pages 605-609.
    3. G. Agarwal & D. Kempe, 2008. "Modularity-maximizing graph communities via mathematical programming," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 66(3), pages 409-418, December.
    4. G. Xu & S. Tsoka & L. G. Papageorgiou, 2007. "Finding community structures in complex networks using mixed integer optimisation," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 60(2), pages 231-239, November.
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