Self-Organizing Map with False-Neighbor Degree between Neurons for Effective Self-Organization

Haruna MATSUSHITA  Yoshifumi NISHIO  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E91-A   No.6   pp.1463-1469
Publication Date: 2008/06/01
Online ISSN: 1745-1337
DOI: 10.1093/ietfec/e91-a.6.1463
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Nonlinear Problems
self-organizing maps,  clustering,  feature extraction,  visualization,  

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In the real world, it is not always true that neighboring houses are physically adjacent or close to each other. in other words, "neighbors" are not always "true neighbors." In this study, we propose a new Self-Organizing Map (SOM) algorithm, SOM with False-Neighbor degree between neurons (called FN-SOM). The behavior of FN-SOM is investigated with learning for various input data. We confirm that FN-SOM can obtain a more effective map reflecting the distribution state of input data than the conventional SOM and Growing Grid.