Snipping for robust k-means clustering under component-wise contamination

被引：16
|
作者：
Farcomeni, Alessio [1 ]
机构：
[1] Univ Roma La Sapienza, Dept Publ Hlth & Infect Dis, I-00185 Rome, Italy
来源：
STATISTICS AND COMPUTING | 2014年 / 24卷 / 06期
关键词：
Clustering; k-Means; Outliers; Robustness; Snipping; Trimming;
D O I：
10.1007/s11222-013-9410-8
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
We introduce the concept of snipping, complementing that of trimming, in robust cluster analysis. An observation is snipped when some of its dimensions are discarded, but the remaining are used for clustering and estimation. Snipped k-means is performed through a probabilistic optimization algorithm which is guaranteed to converge to the global optimum. We show global robustness properties of our snipped k-means procedure. Simulations and a real data application to optical recognition of handwritten digits are used to illustrate and compare the approach.
引用
收藏
页码：907 / 919
页数：13
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