Improving Bregman k-means
by Wesam Ashour; Colin Fyfe
International Journal of Data Mining, Modelling and Management (IJDMMM), Vol. 6, No. 1, 2014

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 and real datasets.

Online publication date: Wed, 02-Jul-2014

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