An efficient clustering algorithm

被引：2

作者：

Zhang, YF ^{[1
]}

Mao, JL ^{[1
]}

Xiong, ZY ^{[1
]}

机构：

[1] Chongqing Univ, Dept Comp Sci, Chongqing 400044, Peoples R China

来源：

2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS | 2003年

关键词：

clustering; the K-means algorithm; squared-error criterion;

D O I：

10.1109/ICMLC.2003.1264483

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Clustering analysis plays an important role in scientific research and commercial application. K-means algorithm is a widely used partition method in clustering. As the dataset's scale increases rapidly, it is difficult to use K-meansand deal with massive data. An improved K-means algorithm is presented. It can avoid getting into locally optimal solution in some degree, and reduce the probability of dividing one big cluster into two or more ones owing to the adoption of squared-error criterion. The experiments demonstrate that the improved K-means is more stable and more accurate.

引用

页码：261 / 265

页数：5

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