Normalizing Constrained Symbolic Data for Clustering

Abstract : Clustering is one of the most common operation in data analysis while constrained is not so common. We present here a clustering method in the framework of Symbolic Data Analysis (S.D.A) which allows to cluster Symbolic Data. Such data can be constrained relations between the variables, expressed by rules which express the domain knowledge. But such rules can induce a combinatorial increase of the computation time according to the number of rules. We present in this paper a way to cluster such data in a quadratic time. This method is based first on the decomposition of the data according to the rules, then we can apply to the data a clustering algorithm based on dissimilarities.
Type de document :
Chapitre d'ouvrage
Rong Guan and Yves Lechevallier and Gilbert Saporta and Huiwen Wang. Advances in Theory and Applications of High Dimensional and Symbolic Data Analysis, RNTI-E-25, Hermann, pp.58-77, 2013, Revue des Nouvelles Technologies de l'Information, 9782705687335
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https://hal.inria.fr/hal-00838658
Contributeur : Brigitte Trousse <>
Soumis le : mercredi 26 juin 2013 - 11:12:23
Dernière modification le : mardi 31 juillet 2018 - 15:04:02

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  • HAL Id : hal-00838658, version 1

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Marc Csernel, Francisco De Carvalho. Normalizing Constrained Symbolic Data for Clustering. Rong Guan and Yves Lechevallier and Gilbert Saporta and Huiwen Wang. Advances in Theory and Applications of High Dimensional and Symbolic Data Analysis, RNTI-E-25, Hermann, pp.58-77, 2013, Revue des Nouvelles Technologies de l'Information, 9782705687335. 〈hal-00838658〉

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