Subspace clustering

Authors

  • Hans-Peter Kriegel,

    Corresponding author
    1. Institut für Informatik, Ludwig-Maximilians-Universität München, Oettingenstrasse 67, D-80538 M, Munich, Germany
    • Institut für Informatik, Ludwig-Maximilians-Universität München, Oettingenstrasse 67, D-80538 M, Munich, Germany
    Search for more papers by this author
  • Peer Kröger,

    1. Institut für Informatik, Ludwig-Maximilians-Universität München, Oettingenstrasse 67, D-80538 M, Munich, Germany
    Search for more papers by this author
  • Arthur Zimek

    1. Institut für Informatik, Ludwig-Maximilians-Universität München, Oettingenstrasse 67, D-80538 M, Munich, Germany
    Search for more papers by this author

Abstract

Subspace clustering refers to the task of identifying clusters of similar objects or data records (vectors) where the similarity is defined with respect to a subset of the attributes (i.e., a subspace of the data space). The subspace is not necessarily (and actually is usually not) the same for different clusters within one clustering solution. In this article, the problems motivating subspace clustering are sketched, different definitions and usages of subspaces for clustering are described, and exemplary algorithmic solutions are discussed. Finally, we sketch current research directions. © 2012 Wiley Periodicals, Inc.

Ancillary