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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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https://hal.archives-ouvertes.fr/hal-00692282
Contributor : Fabrice Rossi <>
Submitted on : Sunday, April 29, 2012 - 6:15:12 PM
Last modification on : Sunday, January 19, 2020 - 6:38:32 PM
Long-term archiving on: : Thursday, December 15, 2016 - 3:28:33 AM

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  • HAL Id : hal-00692282, version 1
  • ARXIV : 1204.6509

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Citation

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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