Title: Improving Bregman k-means

Authors: Wesam Ashour; Colin Fyfe

Addresses: Islamic University of Gaza, Gaza, Palestine ' University of the West of Scotland, High Street, Paisley PA1 2BE, Scotland, UK

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.

Keywords: K-means clustering; local optima; Bregman divergences; clustering algorithms.

DOI: 10.1504/IJDMMM.2014.059981

International Journal of Data Mining, Modelling and Management, 2014 Vol.6 No.1, pp.65 - 82

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 23 Mar 2014 *

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