Open Access
February 2005 A robust method for cluster analysis
María Teresa Gallegos, Gunter Ritter
Ann. Statist. 33(1): 347-380 (February 2005). DOI: 10.1214/009053604000000940


Let there be given a contaminated list of nd-valued observations coming from g different, normally distributed populations with a common covariance matrix. We compute the ML-estimator with respect to a certain statistical model with nr outliers for the parameters of the g populations; it detects outliers and simultaneously partitions their complement into g clusters. It turns out that the estimator unites both the minimum-covariance-determinant rejection method and the well-known pooled determinant criterion of cluster analysis. We also propose an efficient algorithm for approximating this estimator and study its breakdown points for mean values and pooled SSP matrix.


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María Teresa Gallegos. Gunter Ritter. "A robust method for cluster analysis." Ann. Statist. 33 (1) 347 - 380, February 2005.


Published: February 2005
First available in Project Euclid: 8 April 2005

zbMATH: 1064.62074
MathSciNet: MR2157806
Digital Object Identifier: 10.1214/009053604000000940

Primary: 62H30
Secondary: 62F35

Keywords: Breakdown point , cluster analysis , determinant criterion , minimal distance partition , multivariate data , Outliers , robustness

Rights: Copyright © 2005 Institute of Mathematical Statistics

Vol.33 • No. 1 • February 2005
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