Robust Double Clustering: A Method Based on Alternating Concentration Steps

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. 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.