Improving Bregman k-means

Wesam Ashour and Colin Fyfe

International Journal of Data Mining, Modelling and Management, 2014, vol. 6, issue 1, 65-82

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. (search for similar items in EconPapers)
Date: 2014
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