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.
Supported by The Netherlands Organization for Scientific Research (NWO) by grant nr. 575-67-053 for the ‘PIONEER’ project ‘Subject Oriented Multivariate Analysis’ to the first author.
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© 1998 Springer-Verlag Berlin · Heidelberg
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Bensmail, H., Meulman, J.J. (1998). MCMC Inference for Model-based Cluster analysis. In: Rizzi, A., Vichi, M., Bock, HH. (eds) Advances in Data Science and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-72253-0_26
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DOI: https://doi.org/10.1007/978-3-642-72253-0_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64641-9
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