Issues of robustness and high dimensionality in cluster analysis

Kaye Basford (), Geoff McLachlan () and Richard Bean ()
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Kaye Basford: University of Queensland, School of Land and Food Sciences
Geoff McLachlan: University of Queensland, Department of Mathematics & Institute for Molecular Bioscience
Richard Bean: University of Queensland, Institute for Molecular Bioscience

A chapter in Compstat 2006 - Proceedings in Computational Statistics, 2006, pp 3-15 from Springer

Abstract: Abstract Finite mixture models are being increasingly used to model the distributions of a wide variety of random phenomena. While normal mixture models are often used to cluster data sets of continuous multivariate data, a more robust clustering can be obtained by considering the t mixture model-based approach. Mixtures of factor analyzers enable model-based density estimation to be undertaken for high-dimensional data where the number of observations n is very large relative to their dimension p. As the approach using the multivariate normal family of distributions is sensitive to outliers, it is more robust to adopt the multivariate t family for the component error and factor distributions. The computational aspects associated with robustness and high dimensionality in these approaches to cluster analysis are discussed and illustrated.

Keywords: Finite mixture models; normal components; mixtures of factor analyzers; t distributions; EM algorithm (search for similar items in EconPapers)
Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-1709-6_1

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DOI: 10.1007/978-3-7908-1709-6_1

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