Evolutionary fuzzy clustering of relational data
| Contribuinte(s) |
UNIVERSIDADE DE SÃO PAULO |
|---|---|
| Data(s) |
20/10/2012
20/10/2012
2011
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| Resumo |
This paper is concerned with the computational efficiency of fuzzy clustering algorithms when the data set to be clustered is described by a proximity matrix only (relational data) and the number of clusters must be automatically estimated from such data. A fuzzy variant of an evolutionary algorithm for relational clustering is derived and compared against two systematic (pseudo-exhaustive) approaches that can also be used to automatically estimate the number of fuzzy clusters in relational data. An extensive collection of experiments involving 18 artificial and two real data sets is reported and analyzed. (C) 2011 Elsevier B.V. All rights reserved. CNPq Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) FAPESP |
| Identificador |
THEORETICAL COMPUTER SCIENCE, v.412, n.42, p.5854-5870, 2011 0304-3975 http://producao.usp.br/handle/BDPI/28750 10.1016/j.tcs.2011.05.039 |
| Idioma(s) |
eng |
| Publicador |
ELSEVIER SCIENCE BV |
| Relação |
Theoretical Computer Science |
| Direitos |
restrictedAccess Copyright ELSEVIER SCIENCE BV |
| Palavras-Chave | #Natural computing #Fuzzy computing #Evolutionary algorithms #Fuzzy clustering #Relational data #GENE-EXPRESSION DATA #K-MEANS #VALIDITY #CLASSIFICATION #ALGORITHMS #EFFICIENCY #CRITERION #MODEL #SET #AID #Computer Science, Theory & Methods |
| Tipo |
article original article publishedVersion |
