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Abstract: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
* 20-th European Symposium on Artificial Neural Networks, Computational
Intelligence and Machine Learning (ESANN 2012), Bruges : Belgium (2012)