Figure 3 - uploaded by Haruna Matsushita
Content may be subject to copyright.
Context in source publication
Context 1
... data is 2-dimensional random data of 450 points whose distribution is non-uniform as Fig. 3. 225 points are distributed within a small range from 0.2 to 0.45 hor- izontally and from 0.7 to 0.95 vertically. The remaining 225 points are uniformly distributed between 0 and 1. (15 2 15) each, namely, 2 SOMs have totally 445 neurons. The initial values of the learning coecients, the neighborhood co- ecients and the small distance ...
Similar publications
Citations
... 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.
