Competing Behavior of Two Kinds of Self-Organizing Maps and Its Application to Clustering

Haruna MATSUSHITA  Yoshifumi NISHIO  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E90-A   No.4   pp.865-871
Publication Date: 2007/04/01
Online ISSN: 1745-1337
DOI: 10.1093/ietfec/e90-a.4.865
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Neural Networks and Bioengineering
self-organizing maps,  clustering,  data mining,  data extraction,  

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The Self-Organizing Map (SOM) is an unsupervised neural network introduced in the 80's by Teuvo Kohonen. In this paper, we propose a method of simultaneously using two kinds of SOM whose features are different (the nSOM method). Namely, one is distributed in the area at which input data are concentrated, and the other self-organizes the whole of the input space. The competing behavior of the two kinds of SOM for nonuniform input data is investigated. Furthermore, we show its application to clustering and confirm its efficiency by comparing with the k-means method.