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Conference Papers Year : 2012

Dissimilarity Clustering by Hierarchical Multi-Level Refinement

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 relational k-means
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Dates and versions

hal-00692282 , version 1 (29-04-2012)

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Brieuc Conan-Guez, Fabrice Rossi. Dissimilarity Clustering by Hierarchical Multi-Level Refinement. 20-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012), Apr 2012, Bruges, Belgium. pp.483-488. ⟨hal-00692282⟩
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