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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 publié
Date
2009-08
Journal name
Pattern Recognition Letters
Volume
30
Number
11
Publisher
Elsevier
Pages
1037–1045
Publication identifier
http://dx.doi.org/10.1016/j.patrec.2009.04.009
Metadata
Show full item record
Author(s)
De A. T. De Carvalho, Francisco
Csernel, Marc
Lechevallier, Yves
Abstract (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.
Subjects / Keywords
Symbolic Data Analysis; Clustering algorithms; Normal symbolic form; Constraints; Dissimilarity functions
JEL
C02 - Mathematical Methods
C89 - Other

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