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An Exact Algorithm for the Two-Mode KL-Means Partitioning Problem

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  • Michael Brusco
  • Patrick Doreian

Abstract

Two-mode partitioning applications are increasingly common in the physical and social sciences with a variety of models and methods spanning these applications. Two-mode KL-means partitioning (TMKLMP) is one type of two-mode partitioning model with a conceptual appeal that stems largely from the fact that it is a generalization of the ubiquitous (one-mode) K-means clustering problem. A number of heuristic methods have been proposed for TMKLMP, ranging from a two-mode version of the K-means heuristic to metaheuristic approaches based on simulated annealing, genetic algorithms, variable neighborhood search, fuzzy steps, and tabu search. We present an exact algorithm for TMKLMP based on branch-and-bound programming and demonstrate its utility for the clustering of brand switching, manufacturing cell formation, and journal citation data. Although the proposed branchand-bound algorithm does not obviate the need for approximation methods for large two-mode data sets, it does provide a first step in the development of methods that afford a guarantee of globally-optimal solutions for TMKLMP. Copyright Classification Society of North America 2015

Suggested Citation

  • Michael Brusco & Patrick Doreian, 2015. "An Exact Algorithm for the Two-Mode KL-Means Partitioning Problem," Journal of Classification, Springer;The Classification Society, vol. 32(3), pages 481-515, October.
    • Handle: RePEc:spr:jclass:v:32:y:2015:i:3:p:481-515
    DOI: 10.1007/s00357-015-9185-z
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    References listed on IDEAS

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