Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12358/24555
Title | Improving Bregman k-means |
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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 and real datasets. |
Authors | |
Type | Journal Article |
Date | 2014 |
Subjects | |
Published in | International Journal of Data Mining, Modelling and Management |
Series | Volume: 6, Number: 1 |
Publisher | Inderscience Publishers Ltd |
Citation | |
Item link | Item Link |
License | ![]() |
Collections | |
Files in this item | ||
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Ashour, Wesam M._45.pdf | 239.2Kb |