Reliable hierarchical clustering with the self-organizing map

被引：0
|
作者：
Samsonova, EV
Bäck, T
Kok, JN
IJzerman, AP
机构：
[1] Leiden Univ, Amsterdam Ctr Drug Res, NL-2300 RA Leiden, Netherlands
[2] Leiden Univ, Leiden Inst Adv Comp Sci, NL-2333 CA Leiden, Netherlands
[3] NuTech Solut GmbH, D-44227 Dortmund, Germany
来源：
ADVANCES IN INTELLIGENT DATA ANALYSIS VI, PROCEEDINGS | 2005年 / 3646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：
Clustering problems arise in various domains of science and engineering. A large number of methods have been developed to date. 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 a method 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 to select a SOM with the most confident clusters.
引用
收藏
页码：385 / 396
页数：12
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