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 multilevel heuristic refinement. The method is computationally efficient and achieves better quantization errors than the relational k-means.