Presentation 2008-03-28
Lazy Self-Organizing Map for Effective Self-Organization
Taku HARAGUCHI, Haruna MATSUSHITA, Yoshifumi NISHIO,
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Abstract(in English) The Self-Organizing Map (SOM) is a famous algorithm for the unsupervised learning and visualization introduced by Teuvo Kohonen. This study proposes the Lazy Self-Organizing Map (LSOM) algorithm which reflects the world of worker ants. In LSOM, three kinds of neurons exist: worker neurons, lazy neurons and indecisive neurons. We apply LSOM to various input data set and confirm that LSOM can obtain a more effective map reflecting the distribution state of the input data than the conventional SOM.
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Keyword(in English) self-organizing maps (SOM) / clustering / data mining
Paper # NLP2007-168
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Conference Information
Committee NLP
Conference Date 2008/3/21(1days)
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Registration To Nonlinear Problems (NLP)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Lazy Self-Organizing Map for Effective Self-Organization
Sub Title (in English)
Keyword(1) self-organizing maps (SOM)
Keyword(2) clustering
Keyword(3) data mining
1st Author's Name Taku HARAGUCHI
1st Author's Affiliation Department of Electrical and Electronic Engineering, Tokushima University()
2nd Author's Name Haruna MATSUSHITA
2nd Author's Affiliation Department of Electrical and Electronic Engineering, Tokushima University
3rd Author's Name Yoshifumi NISHIO
3rd Author's Affiliation Department of Electrical and Electronic Engineering, Tokushima University
Date 2008-03-28
Paper # NLP2007-168
Volume (vol) vol.107
Number (no) 561
Page pp.pp.-
#Pages 6
Date of Issue