Improving clustering using Bregman divergences

Abstract

We review Bregman divergences and use them in clustering algorithms which we have previously developed to overcome one of the difficulties of the standard k-means algorithm which is its sensitivity to initial conditions which leads to finding sub-optimal local minima. We show empirical results on artificial data sets.