Partitioning hard clustering algorithms based on multiple dissimilarity matrices

Abstract : This paper introduces hard clustering algorithms that are able to partition objects taking into account simultaneously their relational descriptions given by multiple dissimilarity matrices. These matrices have been generated using different sets of variables and dissimilarity functions. These methods are designed to furnish a partition and a prototype for each cluster as well as to learn a relevance weight for each dissimilarity matrix by optimizing an adequacy criterion that measures the fitting between the clusters and their representatives. These relevance weights change at each algorithm iteration and can either be the same for all clusters or different from one cluster to another. Experiments with data sets (synthetic and from UCI machine learning repository) described by real-valued variables as well as with time trajectory data sets show the usefulness of the proposed algorithms.
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Pattern Recognition, Elsevier, 2012, 45 (1), pp.447-464. 〈10.1016/j.patcog.2011.05.016〉
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https://hal.inria.fr/hal-00787879
Contributeur : Nathalie Gaudechoux <>
Soumis le : mercredi 13 février 2013 - 11:13:26
Dernière modification le : mardi 31 juillet 2018 - 15:04:02

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Francisco De Assis Tenorio de Carvalho, Yves Lechevallier, Filipe De Melo. Partitioning hard clustering algorithms based on multiple dissimilarity matrices. Pattern Recognition, Elsevier, 2012, 45 (1), pp.447-464. 〈10.1016/j.patcog.2011.05.016〉. 〈hal-00787879〉

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