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
收藏
页码：1615 / 1620
页数：6
相关论文
共 50 条
  • [21] Multivariate fuzzy k-modes algorithm
    Diêgo B. M. Maciel
    Getulio J. A. Amaral
    Renata M. C. R. de Souza
    Bruno A. Pimentel
    [J]. Pattern Analysis and Applications, 2017, 20 : 59 - 71
  • [22] DP- k-modes: A self-tuning k-modes clustering algorithm
    Xie, Juanying
    Wang, Mingzhao
    Lu, Xiaoxiao
    Liu, Xinglin
    Grant, Philip W.
    [J]. PATTERN RECOGNITION LETTERS, 2022, 158 : 117 - 124
  • [23] A dissimilarity measure for the k-Modes clustering algorithm
    Cao, Fuyuan
    Liang, Jiye
    Li, Deyu
    Bai, Liang
    Dang, Chuangyin
    [J]. KNOWLEDGE-BASED SYSTEMS, 2012, 26 : 120 - 127
  • [24] Optimal mathematical programming and variable neighborhood search for k-modes categorical data clustering
    Xiao, Yiyong
    Huang, Changhao
    Huang, Jiaoying
    Kaku, Ikou
    Xu, Yuchun
    [J]. PATTERN RECOGNITION, 2019, 90 : 183 - 195
  • [25] The k-modes type clustering plus between-cluster information for categorical data
    Bai, Liang
    Liang, Jiye
    [J]. NEUROCOMPUTING, 2014, 133 : 111 - 121
  • [26] Research on Seafood Traceable Data Based on k-Modes Clustering Algorithm
    Ge, Li
    Li, Jiajun
    Chen, Jun
    [J]. JOURNAL OF COASTAL RESEARCH, 2020, : 73 - 77
  • [27] A load clustering algorithm based on discrete wavelet transform and fuzzy K-modes
    Zhang, Jianglin
    Zhang, Yachao
    Hong, Juhua
    Gao, Hongjun
    Liu, Junyong
    [J]. Dianli Zidonghua Shebei/Electric Power Automation Equipment, 2019, 39 (02): : 100 - 106
  • [28] CLEKMODES: a modified k-modes clustering algorithm
    Mastrogiannis, N.
    Giannikos, I.
    Boutsinas, B.
    Antzoulatos, G.
    [J]. JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, 2009, 60 (08) : 1085 - 1095
  • [29] Feature-Weighted Fuzzy K-Modes Clustering
    Nataliani, Yessica
    Yang, Miin-Shen
    [J]. 2019 3RD INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS, METAHEURISTICS & SWARM INTELLIGENCE (ISMSI 2019), 2019, : 63 - 68
  • [30] K-modes clustering
    Chaturvedi, A
    Green, PE
    Carroll, JD
    [J]. JOURNAL OF CLASSIFICATION, 2001, 18 (01) : 35 - 55
12345