Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12358/24708
Title | Improving clustering using Bregman divergences |
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Untitled | |
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 data sets. |
Authors | |
Type | Conference Paper |
Date | 2014 |
Published in | the 11th UK workshop on Computational Intelligence UKCI |
Citation | |
Item link | Item Link |
License | ![]() |
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Files in this item | ||
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Ashour, Wesam M._83.pdf | 12.56Mb |