Finding Hierarchies of Subspace Clusters

  • Elke Achtert
  • Christian Böhm
  • Hans-Peter Kriegel
  • Peer Kröger
  • Ina Müller-Gorman
  • Arthur Zimek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)

Abstract

Many clustering algorithms are not applicable to high-dimensional feature spaces, because the clusters often exist only in specific subspaces of the original feature space. Those clusters are also called subspace clusters. In this paper, we propose the algorithm HiSC (Hierarchical Subspace Clustering) that can detect hierarchies of nested subspace clusters, i.e. the relationships of lower-dimensional subspace clusters that are embedded within higher-dimensional subspace clusters. Several comparative experiments using synthetic and real data sets show the performance and the effectivity of HiSC.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Elke Achtert
    • 1
  • Christian Böhm
    • 1
  • Hans-Peter Kriegel
    • 1
  • Peer Kröger
    • 1
  • Ina Müller-Gorman
    • 1
  • Arthur Zimek
    • 1
  1. 1.Institute for InformaticsLudwig-Maximilians-Universität MünchenGermany

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