Competing and Accommodating Behaviors of Peace SOM

Abstract-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 (ÒSOM), namely, one selforganizes 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 ÒSOM method.In this study, we propose a method of using plural SOMs (TSOM: Tentacled SOM) for effective data extraction, which possesses both abilities of ÒSOM 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.

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“…Û ÄÐ ´Ø · ½ µ Û ÄÐ ´Øµ · ÄÐ ÄÐ ´Øµ´Ü Û ÄÐ ´Øµµ (7) The function ÄÐ ÄÐ ´Øµ is the neighborhood function of ËÇÅ Ä and it is described as follows;…”

Section: B Learning Algorithmmentioning

confidence: 99%

“…We have also proposed the Peace SOM algorithm [7] which remedies the above problem 2). PSOM possesses both competing and accommodating abilities and is used after executing the ÒSOM method.…”

Section: Introductionmentioning

confidence: 99%

Tentacled Self-Organizing Map for Effective Data Extraction

Matsushita

Nishio

2006

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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“…We use the learning function proposed in our past study [10]. The value of the learning function is determined by the distance between the input vector Ü and the weight vector Û of the winner neuron according to;…”

Section: B Learning Functionmentioning

confidence: 99%

“…SOM can classify input data according to similarities and patterns which are obtained by the distance between neurons and is applied to wide fields of data classifications. Although many methods to extract clusters by using SOM have been proposed [3]- [10], it seems to be very difficult to construct a simple method using SOM for universal input data. On the one hand, in the world, the amount and complexity of data increase from year to year.…”

Section: Introductionmentioning

confidence: 99%