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
  • [21] A genetic k-modes algorithm for clustering categorical data
    Gan, GJ
    Yang, ZJ
    Wu, JH
    ADVANCED DATA MINING AND APPLICATIONS, PROCEEDINGS, 2005, 3584 : 195 - 202
  • [22] On the impact of dissimilarity measure in k-modes clustering algorithm
    Ng, Michael K.
    Li, Mark Junjie
    Huang, Joshua Zhexue
    He, Zengyou
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2007, 29 (03) : 503 - 507
  • [23] A Moving Shape-based Robust Fuzzy K-modes Clustering Algorithm for Electricity Profiles
    Liu, Chang
    Wang, Xiaodi
    Huang, Yuan
    Liu, Youbo
    Li, Ran
    Li, Yang
    Liu, Junyong
    ELECTRIC POWER SYSTEMS RESEARCH, 2020, 187
  • [24] The k-modes algorithm with entropy based similarity coefficient
    Sangam, Ravi Sankar
    Om, Hari
    BIG DATA, CLOUD AND COMPUTING CHALLENGES, 2015, 50 : 93 - 98
  • [25] Cluster center initialization algorithm for K-modes clustering
    Khan, Shehroz S.
    Ahmad, Amir
    EXPERT SYSTEMS WITH APPLICATIONS, 2013, 40 (18) : 7444 - 7456
  • [26] A MD fuzzy k-modes Algorithm for Clustering Categorical Matrix-Object Data; [基于分类型矩阵对象数据的MD fuzzy k-modes聚类算法]
    Li S.
    Zhang M.
    Cao F.
    Jisuanji Yanjiu yu Fazhan/Computer Research and Development, 2019, 56 (06): : 1325 - 1337
  • [27] Clustering of Categorical Data Using Intuitionistic Fuzzy k-modes
    Mehta, Darshan
    Tripathy, B. K.
    PROCEEDINGS OF SIXTH INTERNATIONAL CONFERENCE ON SOFT COMPUTING FOR PROBLEM SOLVING (SOCPROS 2016), VOL 1, 2017, 546 : 254 - 263
  • [28] K-modes clustering
    Chaturvedi, A
    Green, PE
    Carroll, JD
    JOURNAL OF CLASSIFICATION, 2001, 18 (01) : 35 - 55
  • [29] K-modes Clustering
    Anil Chaturvedi
    Paul E. Green
    J. Douglas Caroll
    Journal of Classification, 2001, 18 : 35 - 55
  • [30] Rough Set Based Fuzzy K-Modes for Categorical Data
    Saha, Indrajit
    Sarkar, Jnanendra Prasad
    Maulik, Ujjwal
    SWARM, EVOLUTIONARY, AND MEMETIC COMPUTING, (SEMCCO 2012), 2012, 7677 : 323 - 330
12345