Distance matrix based clustering of the Self-Organizing Map

被引：0

作者：

Vesanto, J ^{[1
]}

Sulkava, M ^{[1
]}

机构：

[1] Helsinki Univ Technol, Neural Networks Res Ctr, FIN-02015 Espoo, Finland

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：

Clustering of data is one of the main applications of the Self-Organizing Map (SOM). U-matrix is a commonly used technique to cluster the SOM visually. However, in order to be really useful, clustering needs to be an automated process. There are several techniques which can be used to cluster the SOM autonomously, but the results they provide do not follow the results of U-matrix very well. In this paper, a clustering approach based on distance matrices is introduced which produces results which are very similar to the U-matrix. It is compared to other SOM-based clustering approaches.

引用

页码：951 / 956

页数：6

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