Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Subspace Clustering Techniques

  • Peer KrögerEmail author
  • Arthur Zimek
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_607


Bi-clustering; Co-clustering; Correlation clustering; Oriented clustering; Pattern-based clustering; Projected clustering


Cluster analysis aims at finding a set of subsets (i.e., a clustering) of objects in a data set. A meaningful clustering reflects a natural grouping of the data. In high-dimensional data, irrelevant attributes and correlated attributes make any natural grouping hardly detectable. Specialized techniques aim at finding clusters in subspaces of a high-dimensional data space.

Historical Background

While different weighting of attributes was in use since clusters were derived by hand, the problem of finding a cluster based on a subset of attributes and a specialized solution was first described 1972 by Hartigan [1]. But, triggered by modern capabilities of massive acquisition of high-dimensional data in many scientific and economic domains and the first general approaches to the problem [2, 3, 4], research focused on the problem not till 1998. The...

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Ludwig-Maximilians-Universität MünchenMunichGermany
  2. 2.Department of Mathematics and Computer ScienceUniversity of Southern DenmarkOdenseDenmark