Clustering constrained symbolic data
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 publiéDate
2009-08Nom de la revue
Pattern Recognition LettersVolume
30Numéro
11Éditeur
Elsevier
Pages
1037–1045
Identifiant publication
Métadonnées
Afficher la notice complèteRésumé (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 functionsPublications associées
Affichage des éléments liés par titre et auteur.
-
Silva, K.P.; De A. T. De Carvalho, Francisco; Csernel, Marc (2009-06) Communication / Conférence
-
Silva, K.P.; De A. T. De Carvalho, Francisco; Csernel, Marc (2008) Communication / Conférence
-
da Silva, Alzennyr; De A. T. De Carvalho, Francisco; Lechevallier, Yves; Trousse, Brigitte (2006) Communication / Conférence
-
Rossi, Fabrice; Lechevallier, Yves; Hugueney, Bernard; Hébrail, Georges (2010) Article accepté pour publication ou publié
-
Collectif Revue Des Nouvelles Technologies De L'information,; Diday, Edwin; Saporta, Gilbert; Lechevallier, Yves; Guan, Rong; Wang, Huiwen (2020) Ouvrage