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Cluster Ensembles Based on Vector Space Embeddings of Graphs

  • Kaspar Riesen
  • Horst Bunke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5519)

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.

Keywords

Ensemble Member Cluster Ensemble Graph Domain Cluster Validation Index Graph Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Kaspar Riesen
    • 1
  • Horst Bunke
    • 1
  1. 1.Institute of Computer Science and Applied MathematicsUniversity of BernBernSwitzerland

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