Clustering of Multiple Dissimilarity Data Tables for Documents Categorization

Yves Lechevallier (1) , Francisco de A.T. de Carvalho (1, 2) , Thierry Despeyroux (1) , Filipe M. de Melo (2)

1 AxIS - Usage-centered design, analysis and improvement of information systems
2 CIn - Centro de Informatica UFPE [Recife]

This paper introduces a clustering algorithm 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 a fixed dissimilarity function, using a fixed set of variables and different dissimilarity functions or 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. Experiments aiming at obtaining a categorization of a document data base demonstrate the usefulness of this partitional clustering method.

Mots clés: Clustering Analysis; Relational Data; Documents Categorization

Domaines: Recherche d'information [cs.IR]

Yves Lechevallier, Francisco de A.T. de Carvalho, Thierry Despeyroux, Filipe M. de Melo. Clustering of Multiple Dissimilarity Data Tables for Documents Categorization. COMPSTAT 2010 - 19th International Conference on Computational Statistics, Aug 2010, Paris, France. pp.1263-1270, ⟨10.1007/978-3-7908-2604-3⟩. ⟨inria-00586225⟩