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A robust method for cluster analysis

Maria Teresa Gallegos·Gunter Ritter
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Abstract

Let there be given a contaminated list of n R^d-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 n-r 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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Maria Teresa Gallegos, Gunter Ritter. 2005-04-25. A robust method for cluster analysis. https://doi.org/10.1214/009053604000000940

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