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Un algorithme de classification automatique pour des données relationnelles multi-vues

Abstract : This paper introduces an improvement of a clustering algorithm \citep{decarvalho12} that is able to partition objects taking into account simultaneously their relational descriptions given by multiple dissimilarity matrices. These matrices could have been generated using different sets of variables and dissimilarity functions. This method, which is based on the dynamic hard clustering algorithm for relational data, is designed to provided 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 fit between clusters and their representatives. These relevance weights change at each algorithm iteration and are different from one cluster to another. Moreover, various tools for the partition and cluster interpretation furnished by this new algorithm are also presented. Two experiments demonstrate the usefulness of this clustering method and the merit of the partition and cluster interpretation tools. The first one uses a data set from UCI machine learning repository concerning handwritten numbers (digitalized pictures). The second uses a set of reports for which we have an expert classification given a priori.
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Contributor : Thierry Despeyroux Connect in order to contact the contributor
Submitted on : Monday, May 14, 2012 - 3:43:11 PM
Last modification on : Friday, February 4, 2022 - 3:10:24 AM


  • HAL Id : hal-00697118, version 1



Thierry Despeyroux, Yves Lechevallier, Francisco de A.T. de Carvahlo, Filipe M. de Melo. Un algorithme de classification automatique pour des données relationnelles multi-vues. EGC 2012 - Extraction et Gestion des Connaissances 2012, Jan 2012, Bordeaux, France. pp.125-136. ⟨hal-00697118⟩



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