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International Conference on Discovery Science

DS 2010: Discovery Science pp 159–173Cite as

Integer Linear Programming Models for Constrained Clustering

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6332))

Abstract

We address the problem of building a clustering as a subset of a (possibly large) set of candidate clusters under user-defined constraints. In contrast to most approaches to constrained clustering, we do not constrain the way observations can be grouped into clusters, but the way candidate clusters can be combined into suitable clusterings. The constraints may concern the type of clustering (e.g., complete clusterings, overlapping or encompassing clusters) and the composition of clusterings (e.g., certain clusters excluding others). In the paper, we show that these constraints can be translated into integer linear programs, which can be solved by standard optimization packages. Our experiments with benchmark and real-world data investigates the quality of the clusterings and the running times depending on a variety of parameters.

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Author information

Authors and Affiliations

  1. Institut für Informatik, Technische Universität München, 85748, Garching, Germany

    Marianne Mueller & Stefan Kramer

Authors
  1. Marianne Mueller
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  2. Stefan Kramer
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Waikato, Hamilton, New Zealand

    Bernhard Pfahringer

  2. Department of Computer Science, The University of Waikato, Private Bag 3105, 3240, Hamilton, New Zealand

    Geoff Holmes

  3. School of Computer Science and Engineering, The University of New South Wales, 2052, Sydney, Australia

    Achim Hoffmann

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© 2010 Springer-Verlag Berlin Heidelberg

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Mueller, M., Kramer, S. (2010). Integer Linear Programming Models for Constrained Clustering. In: Pfahringer, B., Holmes, G., Hoffmann, A. (eds) Discovery Science. DS 2010. Lecture Notes in Computer Science(), vol 6332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16184-1_12

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  • DOI: https://doi.org/10.1007/978-3-642-16184-1_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16183-4

  • Online ISBN: 978-3-642-16184-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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