A k-populations algorithm for clustering categorical data

被引：22

作者：

Kim, DW ^{[1
]}

Lee, K

Lee, D

Lee, KH

机构：

[1] Korea Adv Inst Sci & Technol, Dept BioSyst, Taejon 305701, South Korea

[2] Korea Adv Inst Sci & Technol, Adv Informat Technol Res Ctr, Taejon 305701, South Korea

[3] Korea Adv Inst Sci & Technol, Dept Elect Engn & Comp Sci, Taejon 305701, South Korea

来源：

PATTERN RECOGNITION | 2005年 / 38卷 / 07期

关键词：

clustering; categorical data; hierarchical algorithm; k-modes algorithm; fuzzy k-modes algorithm;

D O I：

10.1016/j.patcog.2004.11.017

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, the conventional k-modes-type algorithms for clustering categorical data are extended by representing the clusters of categorical data with k-populations instead of the hard-type centroids used in the conventional algorithms. Use of a population-based centroid representation makes it possible to preserve the uncertainty inherent in data sets as long as possible before actual decisions are made. The k-populations algorithm was found to give markedly better clustering results through various experiments. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

引用

页码：1131 / 1134

页数：4

共 50 条

[41] Fuzzy clustering of categorical data using fuzzy centroids
Kim, DW
Lee, KH
Lee, D
PATTERN RECOGNITION LETTERS, 2004, 25 (11) : 1263 - 1271
[42] Efficiency Based Categorical Data Clustering
Kalaivani, K.
Raghavendra, A. P. V.
2012 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMPUTING RESEARCH (ICCIC), 2012, : 550 - 553
[43] Space Structure and Clustering of Categorical Data
Qian, Yuhua
Li, Feijiang
Liang, Jiye
Liu, Bing
Dang, Chuangyin
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2016, 27 (10) : 2047 - 2059
[44] Clustering Categorical Data Based on Representatives
Aranganayagi, S.
Thangavel, K.
THIRD 2008 INTERNATIONAL CONFERENCE ON CONVERGENCE AND HYBRID INFORMATION TECHNOLOGY, VOL 1, PROCEEDINGS, 2008, : 599 - +
[45] A Categorical Data Clustering Algorithm and Its Efficient Parallel Implementation
Ding, Xiangwu
Tan, Jia
Wang, Mei
PROCEEDINGS OF 2016 5TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT), 2016, : 224 - 228
[46] A fuzzy SV-k-modes algorithm for clustering categorical data with set-valued attributes
Cao, Fuyuan
Huang, Joshua Zhexue
Liang, Jiye
APPLIED MATHEMATICS AND COMPUTATION, 2017, 295 : 1 - 15
[47] A modified Fuzzy k-Partition based on indiscernibility relation for categorical data clustering
Yanto, Iwan Tri Riyadi
Ismail, Maizatul Akmar
Herawan, Tutut
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2016, 53 : 41 - 52
[48] High Dimensional Data Clustering Algorithm Based on Sparse Feature Vector for Categorical Attributes
Wu, Sen
Wei, Guiying
PROCEEDINGS OF 2010 INTERNATIONAL CONFERENCE ON LOGISTICS SYSTEMS AND INTELLIGENT MANAGEMENT, VOLS 1-3, 2010, : 973 - 976
[49] G-ANMI: A mutual information based genetic clustering algorithm for categorical data
Deng, Shengchun
He, Zengyou
Xu, Xiaofei
KNOWLEDGE-BASED SYSTEMS, 2010, 23 (02) : 144 - 149
[50] An LSH-based k-representatives clustering method for large categorical data
Mau, Toan Nguyen
Huynh, Van-Nam
NEUROCOMPUTING, 2021, 463 : 29 - 44

← 1 2 3 4 5 →