Robust Clustering Using Tau-Scales

TL;DR

This paper introduces K Tau Centers, a robust clustering method based on Tau scales, which outperforms traditional K means and adaptive trimmed K means in the presence of outliers, with proven consistency.

Contribution

The paper proposes a novel robust clustering algorithm called K Tau Centers that combines robustness and efficiency adaptively, with theoretical consistency guarantees.

Findings

Performs well in simulation studies

Effective on real data examples

Centers are consistent estimators of true centers

Abstract

K means is a popular non-parametric clustering procedure introduced by Steinhaus (1956) and further developed by MacQueen (1967). It is known, however, that K means does not perform well in the presence of outliers. Cuesta-Albertos et al (1997) introduced a robust alternative, trimmed K means, which can be tuned to be robust or efficient, but cannot achieve these two properties simultaneously in an adaptive way. To overcome this limitation we propose a new robust clustering procedure called K Tau Centers, which is based on the concept of Tau scale introduced by Yohai and Zamar (1988). We show that K Tau Centers performs well in extensive simulation studies and real data examples. We also show that the centers found by the proposed method are consistent estimators of the "true" centers defined as the minimizers of the the objective function at the population level.