7. Model-Based Cluster Analysis for Structured Data

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

Published Online: 25 JAN 2011

DOI: 10.1002/9780470977811.ch7

Cluster Analysis, 5th Edition

Cluster Analysis, 5th Edition

How to Cite

Everitt, B. S., Landau, S., Leese, M. and Stahl, D. (2011) Model-Based Cluster Analysis for Structured Data, in Cluster Analysis, 5th Edition, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470977811.ch7

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:

  • confirmatory factor analysis;
  • finite mixture models;
  • longitudinal data;
  • model-based cluster analysis;
  • structured data

Summary

This chapter discusses model-based framework but considers the implications for finite mixture models for clustering data where the subpopulation means and covariance matrices can be described by a reduced set of parameters because of the special nature of the data. It considers finite mixture models for cluster analysis where the multivariate observations within each cluster can realistically be assumed to have a factor analysis (FA) structure. The chapter applies finite mixture models to the clustering of longitudinal data so that the observed variables arise from some particular model for such data, and where the parameters of this model may differ between clusters. It presents three quite different applications of finite mixture models for factor analysis structured data, and two applications of finite mixture models to data with growth curve structure.

Controlled Vocabulary Terms

confirmatory factor analysis; mixture distribution; mixture model; model-based clustering; panel data; structured questionnaires