Resampling method for unsupervised estimation of cluster validity

被引：171

作者：

Levine, E ^{[1
]}

Domany, E ^{[1
]}

机构：

[1] Weizmann Inst Sci, Dept Phys Complex Syst, IL-76100 Rehovot, Israel

来源：

NEURAL COMPUTATION | 2001年 / 13卷 / 11期

关键词：

D O I：

10.1162/089976601753196030

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We introduce a method for validation of results obtained by clustering analysis of data. The method is based on resampling the available data. A figure of merit that measures the stability of clustering solutions against resampling is introduced. Clusters that are stable against resampling give rise to local maxima of this figure of merit. This is presented first for a one-dimensional data set, for which an analytic approximation for the figure of merit is derived and compared with numerical measurements. Next, the applicability of the method is demonstrated for higher-dimensional data, including gene microarray expression data.

引用

页码：2573 / 2593

页数：21

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