Nonlinear multicriteria clustering based on multiple dissimilarity matrices

Abstract : We present a new algorithm capable of partitioning sets of objects by taking simultaneously into account their relational descriptions given by multiple dissimilarity matrices. The novelty of the algorithm is that it is based on a nonlinear aggregation criterion, weighted Tchebycheff distances, more appropriate than linear combinations (such as weighted averages) for the construction of compromise solutions. We obtain a hard partition of the set of objects, the prototype of each cluster and a weight vector that indicates the relevance of each matrix in each cluster. Since this is a clustering algorithm for relational data, it is compatible with any distance function used to measure the dissimilarity between objects. Results obtained in experiments with data sets (synthetic and real) show the usefulness of the proposed algorithm.
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Article dans une revue
Pattern Recognition, Elsevier, 2013, 46 (12), pp.3383-3394. 〈http://www.sciencedirect.com/science/article/pii/S0031320313002604〉
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https://hal.inria.fr/hal-00917496
Contributeur : Yves Lechevallier <>
Soumis le : mercredi 11 décembre 2013 - 22:07:26
Dernière modification le : vendredi 25 mai 2018 - 12:02:04

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  • HAL Id : hal-00917496, version 1

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Sergio Queiroz, Francisco De Carvalho, Yves Lechevallier. Nonlinear multicriteria clustering based on multiple dissimilarity matrices. Pattern Recognition, Elsevier, 2013, 46 (12), pp.3383-3394. 〈http://www.sciencedirect.com/science/article/pii/S0031320313002604〉. 〈hal-00917496〉

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