Decision of Class Borders on Spherical SOM and Its Visualization

Abstract

In this paper, we propose two methods for determining class borders. One approximates the borders on a Self-Organizing Map (SOM) from the nearest neighbor datasets of different classes. The other especially approximates the borders on a spherical SOM using the coordinate system of a polygon surface. Both methods decide the border on the SOM using the characteristics of the SOM which can map a high dimensional dataset onto a low dimensional map which is usually 2 or 3 dimensional. Using the iris dataset and the wine dataset, it is shown that both proposed methods allow the class borders to be successfully visualized in a comprehensible manner. The verification of the decision border, computed with one of the proposed methods, was performed with the spherical SOM and the dendrogram. The advantages of visualization and the improvement of the accuracy of a cluster analysis were successfully demonstrated using two benchmark databases.