Evolutionary fuzzy clustering of relational data

被引：13

作者：

Horta, Danilo ^{[1
]}

de Andrade, Ivan C. ^{[1
]}

Campello, Ricardo J. G. B. ^{[1
]}

机构：

[1] Univ Sao Paulo, Dept Comp Sci, BR-13560970 Sao Carlos, SP, Brazil

来源：

THEORETICAL COMPUTER SCIENCE | 2011年 / 412卷 / 42期

基金：

巴西圣保罗研究基金会;

关键词：

Natural computing; Fuzzy computing; Evolutionary algorithms; Fuzzy clustering; Relational data; C-MEANS; VALIDITY; EFFICIENCY; CRITERION;

D O I：

10.1016/j.tcs.2011.05.039

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：5854 / 5870

页数：17

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