TreeSOM: Cluster analysis in the self-organizing map

被引：27
作者：
Samsonova, Elena V.
Kok, Joost N.
IJzerman, Ad P.
机构：
[1] Leiden Univ, Div Med Chem, Leiden Amsterdam Ctr Drug Res, NL-2333 CC Leiden, Netherlands
[2] Leiden Univ, Leiden Inst Adv Comp Sci, NL-2333 CA Leiden, Netherlands
来源：
NEURAL NETWORKS | 2006年 / 19卷 / 6-7期
关键词：
self-organizing map; hierarchical clustering; tree; reliability; visualization; tool;
D O I：
10.1016/j.neunet.2006.05.003
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Clustering problems arise in various domains of science and engineering. A large number of methods have been developed to date. The Kohonen self-organizing map (SOM) is a popular tool that maps a high-dimensional space onto a small number of dimensions by placing similar elements close together, forming clusters. Cluster analysis is often left to the user. In this paper we present the method TreeSOM and a set of tools to perform unsupervised SOM cluster analysis, determine cluster confidence and visualize the result as a tree facilitating comparison with existing hierarchical classifiers. We also introduce a distance measure for cluster trees that allows one to select a SOM with the most confident clusters. (c) 2006 Elsevier Ltd. All rights reserved.
引用
收藏
页码：935 / 949
页数：15
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