Weighting variables in K-means clustering

被引：13
|
作者：
Huh, Myung-Hoe [2 ]
Lim, Yong B. [1 ]
机构：
[1] Ewha Womans Univ, Dept Stat, Seoul, South Korea
[2] Korea Univ, Dept Stat, Seoul, South Korea
来源：
JOURNAL OF APPLIED STATISTICS | 2009年 / 36卷 / 01期
关键词：
K-means clustering; variable weighting; penalty constant;
D O I：
10.1080/02664760802382533
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
The aim of this study is to assign weights w1, , wm to m clustering variables Z1, , Zm, so that k groups were uncovered to reveal more meaningful within-group coherence. We propose a new criterion to be minimized, which is the sum of the weighted within-cluster sums of squares and the penalty for the heterogeneity in variable weights w1, , wm. We will present the computing algorithm for such k-means clustering, a working procedure to determine a suitable value of penalty constant and numerical examples, among which one is simulated and the other two are real.
引用
收藏
页码：67 / 78
页数：12
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