Multivariate fuzzy k-modes algorithm

被引：2

作者：

Maciel, Diego B. M. ^{[1
]}

Amaral, Getulio J. A. ^{[2
]}

de Souza, Renata M. C. R. ^{[3
]}

Pimentel, Bruno A. ^{[4
]}

机构：

[1] Univ Fed Amazonas UFAM, Fac Estudos Sociais, Av Gen Rodrigo Octavio Jordao Ramos 3000, BR-69077000 Manaus, AM, Brazil

[2] Univ Fed Pernambuco, Dept Estat, CCEN, Av Prof Luiz Freire S-N Cidade Univ, BR-50740540 Recife, PE, Brazil

[3] Univ Fed Pernambuco, Ctr Informat, Av Prof Luiz Freire S-N Cidade Univ, BR-50740540 Recife, PE, Brazil

[4] Univ Fed Pernambuco, Ctr Informat, Av Jornalista Anibal Fernandes S-N Cidade Univ, BR-50740540 Recife, PE, Brazil

来源：

PATTERN ANALYSIS AND APPLICATIONS | 2017年 / 20卷 / 01期

关键词：

Fuzzy clustering; Unsupervised pattern recognition; Multivariate membership degrees; Categorical data; C-MEANS;

D O I：

10.1007/s10044-015-0465-3

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

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

页码：59 / 71

页数：13

共 50 条

[31] Scalable Laplacian K-modes
Ziko, Imtiaz Masud
Granger, Eric
Ben Ayed, Ismail
[J]. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), 2018, 31
[32] A note on K-modes clustering
Huang, ZX
Ng, MK
[J]. JOURNAL OF CLASSIFICATION, 2003, 20 (02) : 257 - 261
[33] Decremental Possibilistic K-Modes
Ammar, Asma
Elouedi, Zied
Lingras, Pawan
[J]. TWELFTH SCANDINAVIAN CONFERENCE ON ARTIFICIAL INTELLIGENCE (SCAI 2013), 2013, 257 : 15 - 24
[34] Incremental Possibilistic K-Modes
Ammar, Asma
Elouedi, Zied
Lingras, Pawan
[J]. ROUGH SETS, FUZZY SETS, DATA MINING, AND GRANULAR COMPUTING, 2013, 8170 : 293 - 303
[35] A Note on K-modes Clustering
Zhexue Huang
Michael K. Ng
[J]. Journal of Classification, 2003, 20 : 257 - 261
[36] Categorical fuzzy k-modes clustering with automated feature weight learning
Saha, Arkajyoti
Das, Swagatam
[J]. NEUROCOMPUTING, 2015, 166 : 422 - 435
[37] 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
[38] Software cost estimation based on modified K-Modes clustering Algorithm
Bishnu, Partha Sarathi
Bhattacherjee, Vandana
[J]. NATURAL COMPUTING, 2016, 15 (03) : 415 - 422
[39] A Global-Relationship Dissimilarity Measure for the k-Modes Clustering Algorithm
Zhou, Hongfang
Zhang, Yihui
Liu, Yibin
[J]. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2017, 2017
[40] EGA-FMC: enhanced genetic algorithm-based fuzzy k-modes clustering for categorical data
Narasimhan, Medhini
Balasubramanian, Balaji
Kumar, Suryansh D.
Patil, Nagamma
[J]. INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION, 2018, 11 (04) : 219 - 228

← 1 2 3 4 5 →