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Clustering Multi-represented Objects Using Combination Trees

  • Elke Achtert
  • Hans-Peter Kriegel
  • Alexey Pryakhin
  • Matthias Schubert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)

Abstract

When clustering complex objects, there often exist various feature transformations and thus multiple object representations. To cluster multi-represented objects, dedicated data mining algorithms have been shown to achieve improved results. In this paper, we will introduce combination trees for describing arbitrary semantic relationships which can be used to extend the hierarchical clustering algorithm OPTICS to handle multi-represented data objects. To back up the usability of our proposed method, we present encouraging results on real world data sets.

Keywords

Combination Tree Spectral Cluster Reachability Distance Reference Cluster Core Distance 
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 2006

Authors and Affiliations

  • Elke Achtert
    • 1
  • Hans-Peter Kriegel
    • 1
  • Alexey Pryakhin
    • 1
  • Matthias Schubert
    • 1
  1. 1.Institute for Computer ScienceUniversity of MunichGermany

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