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Cluster Ensembles Based on Vector Space Embeddings of Graphs

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Multiple Classifier Systems (MCS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5519))

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Abstract

Cluster ensembles provide us with a versatile alternative to individual clustering algorithms. In structural pattern recognition, however, cluster ensembles have been rarely studied. In the present paper a general methodology for creating structural cluster ensembles is proposed. Our representation formalism is based on graphs and includes strings and trees as special cases. The basic idea of our approach is to view the dissimilarities of an input graph g to a number of prototype graphs as a vectorial description of g. Randomized prototype selection offers a convenient possibility to generate m different vector sets out of the same graph set. Applying any available clustering algorithm to these vector sets results in a cluster ensemble with m clusterings which can then be combined with an appropriate consensus function. In several experiments conducted on different graph sets, the cluster ensemble shows superior performance over two single clustering procedures.

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Author information

Authors and Affiliations

  1. Institute of Computer Science and Applied Mathematics, University of Bern, Neubrückstrasse 10, CH-3012, Bern, Switzerland

    Kaspar Riesen & Horst Bunke

Authors
  1. Kaspar Riesen
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  2. Horst Bunke
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Editors and Affiliations

  1. Faculty of Electrical and Computer Engineering, University of Iceland, Hjardarhagi 2-6, 107, Reykjavik, Iceland

    Jón Atli Benediktsson

  2. Speech and Signal Processing, Guildford, University of Surrey, Centre for Vision, GU2 7XH, Surrey, United Kingdom

    Josef Kittler

  3. Department of Electrical and Electronic Engineering, Piazza d’Armi, University of Cagliari, 09123, Cagliari, Italy

    Fabio Roli

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Riesen, K., Bunke, H. (2009). Cluster Ensembles Based on Vector Space Embeddings of Graphs. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2009. Lecture Notes in Computer Science, vol 5519. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02326-2_22

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  • DOI: https://doi.org/10.1007/978-3-642-02326-2_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02325-5

  • Online ISBN: 978-3-642-02326-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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