Worst-case and smoothed analysis of k-means clustering with Bregman divergences
The <em>k</em>-means method is the method of choice for clustering large-scale data sets and it performs exceedingly well in practice despite its exponential worst-case running-time. To narrow the gap between theory and practice, <em>k</em>-means has been studied in the semi-...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Carleton University
2013-07-01
|
| Series: | Journal of Computational Geometry |
| Online Access: | http://jocg.org/index.php/jocg/article/view/39 |