K-means Clustering Algorithm with improved Initial Center

被引：36
|
作者：
Zhang Chen [1 ]
Xia Shixiong [1 ]
机构：
[1] China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou, Peoples R China
来源：
WKDD: 2009 SECOND INTERNATIONAL WORKSHOP ON KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS | 2009年
关键词：
data clustering; k-means; initial center;
D O I：
10.1109/WKDD.2009.210
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 we present a new clustering method based on k-means that have avoided alternative randomness of initial center. This paper focused on K-means algorithm to the initial value of the dependence of k selected from the aspects of the algorithm is improved. First, the initial clustering number is root N. Second, through the application of the sub-merger strategy the categories were combined. The algorithm does not require the user is given in advance the number of cluster. Experiments on synthetic datasets are presented to have shown significant improvements in clustering accuracy in comparison with the random k-means.
引用
收藏
页码：790 / 792
页数：3
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