A genetic fuzzy k-Modes algorithm for clustering categorical data

被引：78

作者：

Gan, G. ^{[1
]}

Wu, J. ^{[1
]}

Yang, Z. ^{[1
]}

机构：

[1] York Univ, Dept Math & Stat, Toronto, ON M3J 1P3, Canada

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2009年 / 36卷 / 02期

关键词：

Genetic algorithm; k-Modes; Fuzzy logic; Categorical data; CLASSIFICATION;

D O I：

10.1016/j.eswa.2007.11.045

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The fuzzy k-Modes algorithm introduced by Huang and Ng [Huang, Z., & Ng, M. (1999). A fuzzy k-modes algorithm for clustering categorical data. IEEE Transactions on Fuzzy Systems, 7(4), 446-452] is very effective for identifying Cluster structures from categorical data sets. However. the algorithm may stop at locally optimal solutions. In order to search for appropriate fuzzy membership matrices which can minimize the fuzzy objective function, we present a hybrid genetic fuzzy k-Modes algorithm in this paper. To circumvent the expensive crossover operator in genetic algorithms (GAs), we hybridize GA with the fuzzy k-Modes algorithm and define the crossover operator as a one-step fuzzy k-Modes algorithm. Experiments on two real data sets are carried Out to illustrate the performance of the proposed algorithm. (c) 2007 Elsevier Ltd. All rights reserved.

引用

页码：1615 / 1620

页数：6

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