Improving Bregman k-means. (1st January 2014) - Ashour, Wesam; Fyfe, Colin
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 and real datasets.