Dissimilarity Clustering by Hierarchical Multi-Level Refinement
  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 
Brieuc Conan-Guez (LITA), Fabrice Rossi (SAMM)
stat.ML cs.LG