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Synergies of Soft Computing and Statistics for Intelligent Data Analysis pp 573–581Cite as

Merging Partitions Using Similarities of Anchor Subsets

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 190))

Abstract

This paper addresses the problem of merging pairs of partition matrices. Such partition matrices may be produced by collaborative clustering. We assume that each subset in one partition matrix matches one of the subsets in the other partition matrix. To align the arbitrarily ordered rows in the partition matrices we use the memberships of a set of anchor points and maximize their pairwise similarities. Here, we consider various set-theoretic similarity measures. Experiments with a simplified version of the well-known BIRCH benchmark data set illustrate the effectivity of the approach and show that all considered similarity measures are well suited for partition merging.

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Authors and Affiliations

  1. Siemens Corporate Technology, 80200, München, Germany

    Thomas A. Runkler

Authors
  1. Thomas A. Runkler
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Correspondence to Thomas A. Runkler .

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Editors and Affiliations

  1. Faculty of Computer Science, Otto-von-Guericke University of Magdebur, Geb. 29, Raum 008, Universitätsplatz 2, Magdeburg, 39106, Germany

    Rudolf Kruse

  2. , FB Informatik & Informationswissenschaft, University of Konstanz, Konstanz, 78457, Germany

    Michael R. Berthold

  3. of Magdeburg, Faculty of Computer Science, Otto-von-Guericke University, Geb. 29, Universitätsplatz 2 008, Magdeburg, 39106, Germany

    Christian Moewes

  4. , Department of Statistics and OR, University of Oviedo, C/ Calvo Sotelo, s/n, Oviedo, 33007, Spain

    María Ángeles Gil

  5. Systems Research Institute, Polish Academy of Sciences, Newelska 6, Warsaw, 01-447, Poland

    Przemysław Grzegorzewski

  6. Systems Research Institute, Polish Academy of Sciences, Newelska 6, Warsaw, 01-447, Poland

    Olgierd Hryniewicz

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Runkler, T.A. (2013). Merging Partitions Using Similarities of Anchor Subsets. In: Kruse, R., Berthold, M., Moewes, C., Gil, M., Grzegorzewski, P., Hryniewicz, O. (eds) Synergies of Soft Computing and Statistics for Intelligent Data Analysis. Advances in Intelligent Systems and Computing, vol 190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33042-1_61

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  • DOI: https://doi.org/10.1007/978-3-642-33042-1_61

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33041-4

  • Online ISBN: 978-3-642-33042-1

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