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Trimming algorithms for clustering contaminated grouped data and their robustness

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Abstract

We establish an affine equivariant, constrained heteroscedastic model and criterion with trimming for clustering contaminated, grouped data. We show existence of the maximum likelihood estimator, propose a method for determining an appropriate constraint, and design a strategy for finding reasonable clusterings. We finally compute breakdown points of the estimated parameters thereby showing asymptotic robustness of the method.

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Authors and Affiliations

  1. Institute for Data Analysis, Salzweg, Germany

    María Teresa Gallegos

  2. Fakultät für Informatik und Mathematik, Universität Passau, Passau, Germany

    Gunter Ritter

Authors
  1. María Teresa Gallegos
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  2. Gunter Ritter
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Correspondence to Gunter Ritter.

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Gallegos, M.T., Ritter, G. Trimming algorithms for clustering contaminated grouped data and their robustness. Adv Data Anal Classif 3, 135–167 (2009). https://doi.org/10.1007/s11634-009-0044-9

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  • DOI: https://doi.org/10.1007/s11634-009-0044-9

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