The global k-means clustering algorithm

被引：78

作者：

Likas, A ^{[1
]}

Vlassis, N

Verbeek, JJ

机构：

[1] Univ Ioannina, Dept Comp Sci, GR-45110 Ioannina, Greece

[2] Univ Amsterdam, Inst Comp Sci, NL-1098 SJ Amsterdam, Netherlands

来源：

PATTERN RECOGNITION | 2003年 / 36卷 / 02期

关键词：

clustering; k-means algorithm; global optimization; k-d trees; data mining;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present the global k-means algorithm which is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure consisting of N (with N being the size of the data set) executions of the k-means algorithm from suitable initial positions. We also propose modifications of the method to reduce the computational load without significantly affecting solution quality. The proposed clustering methods are tested on well-known data sets and they compare favorably to the k-means algorithm with random restarts. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

引用

页码：451 / 461

页数：11

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