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

Authors:
Haruna Matsushita;Yoshifumi Nishio
Affiliations:
-;-
Venue:
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Year:
2008

Citing 3
Cited 1

Self-organizing maps

Self-organizing maps
Clustering of the self-organizing map

IEEE Transactions on Neural Networks
Rival-Model Penalized Self-Organizing Map

IEEE Transactions on Neural Networks

Learning topological constraints in self-organizing map

ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.