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Data Analysis and Information Systems pp 24–40Cite as

Computational Methods in Clustering from a Mathematical Programming Viewpoint

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Summary

Cluster Analysis is at the crossroad of many disciplines, and has numerous and diverse methods and applications. Mathematical Programming (together with graph theory, complexity theory and data structures) can be used to give a coherent view of the field. This leads to define clustering paradigms, specify problems within these paradigms as mathematical programs, study their complexity, design polynomial algorithms for easy problems, non polynomial ones or heuristics for hard ones and evaluate results obtained. An overview of this line of research is given, stressing exact algorithms.

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Author information

Authors and Affiliations

  1. Dept. MQ, École des Hautes Études Commerciales, 5255 avenue Decelles, Montréal, Québec, H3T 1V6, Canada

    Pierre Hansen

  2. École Polytechnique de Montréal, Montréal, Canada

    Brigitte Jaumard

Authors
  1. Pierre Hansen
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  2. Brigitte Jaumard
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Editors and Affiliations

  1. Institut für Statistik und Wirtschaftsmathematik, Rheinisch-Westfälische Technische Hochschule Aachen (RWTH), Wüllnerstr. 3, D-52056, Aachen, Germany

    Hans-Hermann Bock

  2. Institut für Statistik und Ökonometrie, Universität Basel, Holbeinstr. 12, CH-4051, Basel, Switzerland

    Wolfgang Polasek

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© 1996 Springer-Verlag Berlin · Heidelberg

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Hansen, P., Jaumard, B. (1996). Computational Methods in Clustering from a Mathematical Programming Viewpoint. In: Bock, HH., Polasek, W. (eds) Data Analysis and Information Systems. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-80098-6_3

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  • DOI: https://doi.org/10.1007/978-3-642-80098-6_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60774-8

  • Online ISBN: 978-3-642-80098-6

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