Chapter

Knowledge Discovery in Databases: PKDD 2006

Volume 4213 of the series Lecture Notes in Computer Science pp 446-453

Finding Hierarchies of Subspace Clusters

  • Elke AchtertAffiliated withInstitute for Informatics, Ludwig-Maximilians-Universität München
  • , Christian BöhmAffiliated withInstitute for Informatics, Ludwig-Maximilians-Universität München
  • , Hans-Peter KriegelAffiliated withInstitute for Informatics, Ludwig-Maximilians-Universität München
  • , Peer KrögerAffiliated withInstitute for Informatics, Ludwig-Maximilians-Universität München
  • , Ina Müller-GormanAffiliated withInstitute for Informatics, Ludwig-Maximilians-Universität München
  • , Arthur ZimekAffiliated withInstitute for Informatics, Ludwig-Maximilians-Universität München

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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.