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

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