5. Optimization Clustering Techniques

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

Published Online: 25 JAN 2011

DOI: 10.1002/9780470977811.ch5

Cluster Analysis, 5th Edition

Cluster Analysis, 5th Edition

How to Cite

Everitt, B. S., Landau, S., Leese, M. and Stahl, D. (2011) Optimization Clustering Techniques, in Cluster Analysis, 5th Edition, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470977811.ch5

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 techniques;
  • dissimilarity matrix;
  • optimization methods

Summary

This chapter considers a class of clustering techniques which produces a partition of the individuals into a specified number of groups, by either minimizing or maximizing some numerical criterion. The basic idea behind the methods to be described in the chapter is that associated with each partition of the n individuals into the required number of groups, g, is an index c(n, g), the value of which measures some aspect of the “quality” of this particular partition. The chapter introduces cluster criteria derived from a dissimilarity matrix followed by criteria derived directly from continuous variables, and then discusses algorithms that can be used to optimize these criteria. Finally, the chapter presents several examples of applications of cluster optimization methods.

Controlled Vocabulary Terms

cluster analysis