Tentacled Self-Organizing Map for Effective Data Extraction(Neuron and Neural Networks,<Special Section>Nonlinear Theory and its Applications)

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概要

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• Since we can accumulate a large amount of data including useless information in recent years, it is important to investigate various extraction method of clusters from data including much noises. The Self-Organizing Map (SOM) has attracted attention for clustering nowadays. In this study, we propose a method of using plural SOMs (TSOM: Tentacled SOM) for effective data extraction. TSOM consists of two kinds of SOM whose features are different, namely, one self-organizes the area where input data are concentrated, and the other self-organizes the whole of the input space. Each SOM of TSOM can catch the information of other SOMs existing in its neighborhood and self-organizes with the competing and accommodating behaviors. We apply TSOM to data extraction from input data including much noise, and can confirm that TSOM successfully extracts only clusters even in the case that we do not know the number of clusters in advance.

• 社団法人電子情報通信学会の論文
• 2007-10-01

著者

• NISHIO Yoshifumi

  Department of Electrical and Electronic Engineering, Tokushima University

• Nishio Yoshifumi

  Faculty Of Engineering Tokushima University

• NISHIO Yoshifumi

  the Faculty of Engineering, Tokushima University

• Nishio Yoshifumi

  Electrical And Electronic Engineering The University Of Tokushima

• MATSUSHITA Haruna

  Department of Electrical and Electronic Engineering, Tokushima University

• Nishio Yoshifumi

  Department Of Dept. Of Electrical And Electronics Engineering Tokushima University

• Nishio Yoshifumi

  The Faculty Of Science And Technology Keio University

• Matsushita Haruna

  Department Of Electrical And Electronic Engineering Tokushima University

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