Multivariate fuzzy k-modes algorithm

被引：2

作者：

Maciel, Diego B. M. ^{[1
]}

Amaral, Getulio J. A. ^{[2
]}

de Souza, Renata M. C. R. ^{[3
]}

Pimentel, Bruno A. ^{[4
]}

机构：

[1] Univ Fed Amazonas UFAM, Fac Estudos Sociais, Av Gen Rodrigo Octavio Jordao Ramos 3000, BR-69077000 Manaus, AM, Brazil

[2] Univ Fed Pernambuco, Dept Estat, CCEN, Av Prof Luiz Freire S-N Cidade Univ, BR-50740540 Recife, PE, Brazil

[3] Univ Fed Pernambuco, Ctr Informat, Av Prof Luiz Freire S-N Cidade Univ, BR-50740540 Recife, PE, Brazil

[4] Univ Fed Pernambuco, Ctr Informat, Av Jornalista Anibal Fernandes S-N Cidade Univ, BR-50740540 Recife, PE, Brazil

来源：

PATTERN ANALYSIS AND APPLICATIONS | 2017年 / 20卷 / 01期

关键词：

Fuzzy clustering; Unsupervised pattern recognition; Multivariate membership degrees; Categorical data; C-MEANS;

D O I：

10.1007/s10044-015-0465-3

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the fuzzy k-modes clustering, there is just one membership degree of interest by class for each individual which cannot be sufficient to model ambiguity of data precisely. It is known that the essence of a multivariate thinking allows to expose the inherent structure and meaning revealed within a set of variables classified. In this paper, a multivariate approach for membership degrees is presented to better handle ambiguous data that share properties of different clusters. This method is compared with other fuzzy k-modes methods of the literature based on a multivariate internal index that is also proposed in this paper. Synthetic and real categorical data sets are considered in this study.

引用

页码：59 / 71

页数：13

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