8. Miscellaneous Clustering Methods

  1. Brian S. Everitt,
  2. Sabine Landau,
  3. Morven Leese and
  4. Daniel Stahl

Published Online: 25 JAN 2011

DOI: 10.1002/9780470977811.ch8

Cluster Analysis, 5th Edition

Cluster Analysis, 5th Edition

How to Cite

Everitt, B. S., Landau, S., Leese, M. and Stahl, D. (2011) Miscellaneous Clustering Methods, in Cluster Analysis, 5th Edition, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470977811.ch8

Author Information

  1. King's College London, UK

Publication History

  1. Published Online: 25 JAN 2011
  2. Published Print: 7 JAN 2011

Book Series:

  1. Wiley Series in Probability and Statistics

Book Series Editors:

  1. Walter A. Shewhart and
  2. Samuel S. Wilks

ISBN Information

Print ISBN: 9780470749913

Online ISBN: 9780470977811

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Keywords:

  • clustering methods;
  • constrained clustering;
  • density search;
  • fuzzy clustering;
  • mode analysis;
  • neural network;
  • overlapping clusters

Summary

This chapter discusses density search and mode analysis, where clusters are assumed to be concentrated in relatively dense patches in a metric space. It talks about methods which allow overlapping clusters, including pyramids. The chapter focuses on direct clustering of data matrices, rather than proximity matrices, in order to cluster both variables and objects simultaneously. It explains constrained clustering, where the membership of clusters is determined partly by external information, for example spatial contiguity. In fuzzy methods the objects are not assigned to a particular cluster but possess a membership function indicating the strength of membership to each cluster. The chapter then presents neural networks: pattern recognition algorithms that imitate the computational capabilities of large, highly connected networks such as the neurons in the human brain.

Controlled Vocabulary Terms

Constrained clustering; density-based clustering; FLAME clustering; fuzzy clustering; total correlation