Dissimilarity Clustering by Hierarchical Multi-Level Refinement
Conan-Guez, Brieuc, Rossi, Fabrice
We introduce in this paper a new way of optimizing the natural extension of the quantization error using in k-means clustering to dissimilarity data. The proposed method is based on hierarchical clustering analysis combined with multi-level heuristic refinement. The method is computationally efficient and achieves better quantization errors than the
Apr-29-2012
- Country:
- Europe (0.47)
- North America > United States
- New York (0.14)
- Genre:
- Research Report (0.40)
- Technology: