Clustering constrained symbolic data

被引：16

作者：

de Carvalho, Francisco de A. T. ^{[1
]}

Csernel, Marc ^{[2
,3
]}

Lechevallier, Yves ^{[2
]}

机构：

[1] CIn UFPE, Ctr Informat, BR-50740540 Recife, PE, Brazil

[2] INRIA, F-78153 Le Chesnay, France

[3] Univ Paris 09, F-75775 Paris 16, France

来源：

PATTERN RECOGNITION LETTERS | 2009年 / 30卷 / 11期

关键词：

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

D O I：

10.1016/j.patrec.2009.04.009

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2009 Elsevier B.V. All rights reserved.

引用

页码：1037 / 1045

页数：9

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