Self-Organizing Maps
Competing Behavior of Two Kinds of Self-Organizing Maps and Its Application to Clustering
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Unsupervised speaker recognition based on competition between self-organizing maps
IEEE Transactions on Neural Networks
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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.