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Tentacled Self-Organizing Map for Effective Data Extraction
Haruna MATSUSHITA Yoshifumi NISHIO
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2007/10/01
Online ISSN: 1745-1337
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on Nonlinear Theory and its Applications)
Category: Neuron and Neural Networks
self-organizing maps, clustering, data extraction, data segmentation,
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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.