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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Communication dans un congrès
20-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012), Apr 2012, Bruges, Belgium. pp.483-488, 2012
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https://hal.archives-ouvertes.fr/hal-00692282
Contributeur : Fabrice Rossi <>
Soumis le : dimanche 29 avril 2012 - 18:15:12
Dernière modification le : mercredi 18 mai 2016 - 01:05:49
Document(s) archivé(s) le : jeudi 15 décembre 2016 - 03:28:33

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

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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, 2012. <hal-00692282>

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