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
收藏
页码：1131 / 1134
页数：4
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