Clustering constrained symbolic data

Authors:
Francisco de A. T. de Carvalho;Marc Csernel;Yves Lechevallier
Affiliations:
Centro de Informatica, CIn/UFPE, Av. Prof Luiz Freire, s/n, Cidade Universitaria, CEP 50.740-540, Recife, PE, Brazil;INRIA, Rocquencourt, Domaine de Voluceau, Rocquencourt, B.P. 105, 78153 le Chesnay Cedex, France and University of Paris-Dauphine, Place du Maréchal de Lattre de Tassigny, 75775 Paris Cedex 1 ...;INRIA, Rocquencourt, Domaine de Voluceau, Rocquencourt, B.P. 105, 78153 le Chesnay Cedex, France
Venue:
Pattern Recognition Letters
Year:
2009

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Abstract

Dealing with multi-valued data has become quite common in both the framework of databases as well as data analysis. Such data can be constrained by domain knowledge provided by relations between the variables and these relations are expressed by rules. However, such knowledge can introduce a combinatorial increase in the computation time depending on the number of rules. In this paper, we present a way to cluster such data in polynomial time. The method is based on the following: a decomposition of the data according to the rules, a suitable dissimilarity function and a clustering algorithm based on dissimilarities.