Tentacled Self-Organizing Map for Effective Data Extraction

Since we can accumulate a huge amount of data including useless information in these years, it is important to investigate various extraction method of clusters from data including a lot of noises. The self-organizing map (SOM) attracts attentions for clustering in these years. In our past study, we have proposed a method of using simultaneously two kinds of SOMs whose features are different (nSOM), namely, one self-organizes the area on which input data are concentrated, and the other self-organizes the whole of the input space. Further, we have applied this method to clustering of data including a lot of noises and have confirmed the efficiency. However, in order to obtain an efficient clustering performance using this method, we must determine the appropriate number of the SOMs used in the method. This problem has been remedied by proposing the Peace SOM (PSOM) method, however, PSOM algorithm must be used after executing the nSOM method. In this study, we propose a method of using plural SOMs (TSOM: Tentacled SOM) for effective data extraction, which possesses both abilities of nSOM and PSOM. Each SOM of TSOM can catch the information of other SOMs existing in its neighborhood and self-organizes with the competing and accommodating behaviors. The behavior of TSOM is investigated with applications to data extraction from input data including a lot of noises. We can confirm that TSOM successfully extracts clusters even in the case that we do not know the number of clusters in advance.