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... simulation result is shown in Fig. 4. From Fig. 4(a), we can say that One SOM is self-organized. How- ever, Two SOMs can be self-organized more eectively as shown in Fig. 4(b). In addition, SOM L is self-organized in the area for which input data concentrate, and SOM G is self-organized over the whole input space. ...
Context 2
... simulation result is shown in Fig. 4. From Fig. 4(a), we can say that One SOM is self-organized. How- ever, Two SOMs can be self-organized more eectively as shown in Fig. 4(b). In addition, SOM L is self-organized in the area for which input data concentrate, and SOM G is self-organized over the whole input space. ...
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... Further, since we can accumulate a huge amount of data including useless information in these years, it is important to investigate various extraction methods of clusters from data including a lot of noises. In our past study, we have investigated the basic features of using two kinds of SOMs whose features are different [6]. We have confirmed that the two SOMs could extract the features of 2-dimensional nonuniform input data. ...
The Self-Organizing Map (SOM) is an unsupervised neural network introduced in the 80's by Teuvo Kohonen. In this paper, we propose a method of using simultaneously two kinds of SOMs whose features are different. Namely, one is distributed in the area on which input data are concentrated, and the other self-organizes the whole of the input space. The competing behavior of the two kinds of SOMs for nonuniform input data is investigated. Furthermore, we show its application to clustering and confirm the efficiency by comparing with the k-means method.
