Papers › Statistical analysis of a hierarchical clustering algorithm with outliers
Statistical analysis of a hierarchical clustering algorithm with outliers
Nicolas Klutchnikoff, Audrey Poterie, Laurent Rouviere
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It is well known that the classical single linkage algorithm usually fails to identify clusters in the presence of outliers. In this paper, we propose a new version of this algorithm, and we study its mathematical performances. In particular, we establish an oracle type inequality which ensures that our procedure allows to recover the clusters with large probability under minimal assumptions on the distribution of the outliers. We deduce from this inequality the consistency and some rates of convergence of our algorithm for various situations. Performances of our approach is also assessed through simulation studies and a comparison with classical clustering algorithms on simulated data is also presented.
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