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.