Self-Organizing Map Considering False Neighboring Neuron
Haruna Matsushita, Yoshifumi Nishio · 2007
In the real world, it is not always true that the next-door house is close to my house, in other words, "neighbors" are not always "true neighbors". In this study, we propose a new self-organizing map (SOM) algorithm which considers the false neighboring neuron (called FNN-SOM). The FNN-SOM self-organizes with considering the real neighboring relation. The behavior of FNN-SOM is investigated with learning for various input data. We confirm that we can obtain the more effective map reflecting the distribution state of input data than the conventional SOM.