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Robust Double Clustering: A Method Based on Alternating Concentration Steps

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Abstract

We propose two algorithms for robust two-mode partitioning of a data matrix in the presence of outliers. First we extend the robust k-means procedure to the case of biclustering, then we slightly relax the definition of outlier and propose a more flexible and parsimonious strategy, which anyway is inherently less robust. We discuss the breakdown properties of the algorithms, and illustrate the methods with simulations and three real examples.

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Authors and Affiliations

  1. University of Rome ”La Sapienza”, Piazzale Aldo Moro, 5, 00186, Roma, Italy

    Alessio Farcomeni

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  1. Alessio Farcomeni
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Correspondence to Alessio Farcomeni.

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The author is grateful to four referees for detailed suggestions that led to an improved paper, and to Professor Vichi for support and careful reading of a first draft. Acknowledgements go also to Francesca Martella for advice.

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Farcomeni, A. Robust Double Clustering: A Method Based on Alternating Concentration Steps. J Classif 26, 77–101 (2009). https://doi.org/10.1007/s00357-009-9026-z

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