Stability-based model order selection in clustering with applications to gene expression data

被引：0

作者：

Roth, V ^{[1
]}

Braun, ML ^{[1
]}

Lange, T ^{[1
]}

Buhmann, JM ^{[1
]}

机构：

[1] Univ Bonn, Inst Comp Sci, Dept 3, D-53117 Bonn, Germany

来源：

ARTIFICIAL NEURAL NETWORKS - ICANN 2002 | 2002年 / 2415卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The concept of cluster stability is introduced to assess the validity of data partitionings found by clustering algorithms. It allows us to explicitly quantify the quality of a clustering solution, without being dependent on external information. The principle of maximizing the cluster stability can be interpreted as choosing the most self-consistent data partitioning. We present an empirical estimator for the theoretically derived stability index, based on resampling. Experiments are conducted on well known gene expression data sets, re-analyzing the work by Alon et al. [1] and by Spellman et al. [8].

引用

页码：607 / 612

页数：6

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