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Constrained Clustering Via Concavity Cuts

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Book cover Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4510))

Abstract

In this paper, we adapt Tuy’s concave cutting plane method to the problem of finding an optimal grouping of semi-supervised clustering. We also give properties of local optimal solutions to the semi-supervised clustering. On test data sets with up to 1500 points, our algorithm typically find a solution with objective value around 2% smaller of the initial function value than that obtained by k-means algorithm within 4 seconds, although the run time is hundred times of that of the k-means algorithm.

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Authors and Affiliations

  1. Center of Operations Research and Econometrics (CORE), Université catholique de Louvain, 34 Voie du Roman Pays, 1348 Louvain-la-Neuve, Belgium

    Yu Xia

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  1. Yu Xia
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Pascal Van Hentenryck Laurence Wolsey

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© 2007 Springer Berlin Heidelberg

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Xia, Y. (2007). Constrained Clustering Via Concavity Cuts. In: Van Hentenryck, P., Wolsey, L. (eds) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. CPAIOR 2007. Lecture Notes in Computer Science, vol 4510. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72397-4_23

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  • DOI: https://doi.org/10.1007/978-3-540-72397-4_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72396-7

  • Online ISBN: 978-3-540-72397-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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