Multivariate fuzzy k-modes algorithm

被引：0
|
作者：
Diêgo B. M. Maciel
Getulio J. A. Amaral
Renata M. C. R. de Souza
Bruno A. Pimentel
机构：
[1] Universidade Federal do Amazonas (UFAM),Faculdade de Estudos Sociais
[2] CCEN,Departamento de Estatística
[3] Universidade Federal de Pernambuco,Centro de Informática
[4] Universidade Federal de Pernambuco,Centro de Informática
[5] Universidade Federal de Pernambuco,undefined
来源：
Pattern Analysis and Applications | 2017年 / 20卷
关键词：
Fuzzy clustering; Unsupervised pattern recognition; Multivariate membership degrees; Categorical data;
D O I：
暂无
中图分类号：
学科分类号：
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
收藏
页码：59 / 71
页数：12
相关论文
共 50 条
  • [1] Multivariate fuzzy k-modes algorithm
    Maciel, Diego B. M.
    Amaral, Getulio J. A.
    de Souza, Renata M. C. R.
    Pimentel, Bruno A.
    PATTERN ANALYSIS AND APPLICATIONS, 2017, 20 (01) : 59 - 71
  • [2] A Multivariate Fuzzy Weighted K-Modes Algorithm with Probabilistic Distance for Categorical Data
    Kuo, Ren-Jieh
    Cendana, Maya
    Nguyen, Thi Phuong Quyen
    Zulvia, Ferani E.
    JOURNAL OF ICT RESEARCH AND APPLICATIONS, 2024, 18 (02) : 93 - 107
  • [3] Block Fuzzy K-modes Clustering Algorithm
    Yang, Miin-Shen
    Lin, Chih-Ying
    2009 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3, 2009, : 384 - 389
  • [4] A fuzzy k-modes algorithm for clustering categorical data
    Huang, ZX
    Ng, MK
    IEEE TRANSACTIONS ON FUZZY SYSTEMS, 1999, 7 (04) : 446 - 452
  • [5] A Bio Inspired Fuzzy K-Modes Clustring Algorithm
    Soliman, Omar S.
    Saleh, Doaa A.
    Rashwan, Samaa
    NEURAL INFORMATION PROCESSING, ICONIP 2012, PT III, 2012, 7665 : 663 - 669
  • [6] A genetic fuzzy k-Modes algorithm for clustering categorical data
    Gan, G.
    Wu, J.
    Yang, Z.
    EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (02) : 1615 - 1620
  • [7] Genetic intuitionistic weighted fuzzy k-modes algorithm for categorical data
    Kuo, R. J.
    Thi Phuong Quyen Nguyen
    NEUROCOMPUTING, 2019, 330 : 116 - 126
  • [8] DP- k-modes: A self-tuning k-modes clustering algorithm
    Xie, Juanying
    Wang, Mingzhao
    Lu, Xiaoxiao
    Liu, Xinglin
    Grant, Philip W.
    PATTERN RECOGNITION LETTERS, 2022, 158 : 117 - 124
  • [9] Application of metaheuristic based fuzzy K-modes algorithm to supplier clustering
    Kuo, R. J.
    Potti, Yuliana
    Zulvia, Ferani E.
    COMPUTERS & INDUSTRIAL ENGINEERING, 2018, 120 : 298 - 307
  • [10] A load clustering algorithm based on discrete wavelet transform and fuzzy K-modes
    Zhang J.
    Zhang Y.
    Hong J.
    Gao H.
    Liu J.
    Dianli Zidonghua Shebei/Electric Power Automation Equipment, 2019, 39 (02): : 100 - 106and122
12345