Dissimilarity Clustering by Hierarchical Multi-Level Refinement
The European Symposium on Artificial Neural Networks (ESANN), 2012
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
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