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|Title||Improving clustering using Bregman divergences|
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.
|Published in||the 11th UK workshop on Computational Intelligence UKCI|
|Item link||Item Link|
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|Ashour, Wesam M._83.pdf||12.56Mb|