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MCMC Inference for Model-based Cluster analysis

  • Halima Bensmail
  • Jacqueline J. Meulman
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

A new approach to cluster analysis has been introduced based on parsimonious geometric modelling of the within-group covariance matrices in a mixture of multivariate normal distributions, using Bayesian calculation and the Gibbs sampler. The approach answers many limitations of the Maximum likelihood approach. Here we propose to investigate more general models dealing with the shape and orientation of the clusters using the Gibbs Sampler.

Keywords

Bayes Factor Gaussian Mixture Gibbs Sampler 

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Copyright information

© Springer-Verlag Berlin · Heidelberg 1998

Authors and Affiliations

  • Halima Bensmail
    • 1
  • Jacqueline J. Meulman
    • 1
  1. 1.Department of Education, Data Theory GroupLeiden UniversityLeidenThe Netherlands

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