Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12358/24555
| Title | Improving Bregman k-means |
|---|---|
| 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. |
| 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 | ||
|---|---|---|
| Ashour, Wesam M._45.pdf | 239.2Kb | |
