Issues of robustness and high dimensionality in cluster analysis

Basford, Kaye, McLachlan, Geoff and Bean, Richard (2006). Issues of robustness and high dimensionality in cluster analysis. In: A. Rizzi and M. Vichi, COMPSTAT2006: Proceedings in Computational Statistics. 17th Symposium on Computational Statistics (COMSTAT 2006), Rome, Italy, (3-15). 28 August - 1 September 2006. doi:10.1007/978-3-7908-1709-6_1

Author Basford, Kaye
McLachlan, Geoff
Bean, Richard
Title of paper Issues of robustness and high dimensionality in cluster analysis
Conference name 17th Symposium on Computational Statistics (COMSTAT 2006)
Conference location Rome, Italy
Conference dates 28 August - 1 September 2006
Proceedings title COMPSTAT2006: Proceedings in Computational Statistics
Journal name COMPSTAT 2006: Proceedings in Computational Statistics
Place of Publication Rome, Italy
Publisher Physica-Verlag
Publication Year 2006
Year available 2006
Sub-type Fully published paper
DOI 10.1007/978-3-7908-1709-6_1
Open Access Status DOI
ISBN 3790817082
Editor A. Rizzi
M. Vichi
Start page 3
End page 15
Total pages 13
Language eng
Abstract/Summary 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.
Subjects E1
230203 Statistical Theory
230204 Applied Statistics
780101 Mathematical sciences
Keyword Computer Science, Artificial Intelligence
Statistics & Probability
Computer Science
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Unknown

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Created: Fri, 24 Aug 2007, 08:00:41 EST