Clustering constrained symbolic data

De A. T. De Carvalho, Francisco; Csernel, Marc; Lechevallier, Yves

De A. T. De Carvalho, Francisco; Csernel, Marc; Lechevallier, Yves (2009), Clustering constrained symbolic data, Pattern Recognition Letters, 30, 11, p. 1037–1045. http://dx.doi.org/10.1016/j.patrec.2009.04.009

Type

Article accepté pour publication ou publié

Date

2009-08

Nom de la revue

Pattern Recognition Letters

Volume

Numéro

Éditeur

Elsevier

Pages

1037–1045

Résumé (EN)

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.

Mots-clés

Symbolic Data Analysis; Clustering algorithms; Normal symbolic form; Constraints; Dissimilarity functions

JEL

C02 - Mathematical Methods
C89 - Other