A Study on Development Performance of Self-Organizing Map for Big Data Analysis

Abstract

A self-organizing map (SOM) is known as a good tool to discover and visualize underlying rules in a given data set. In this study, it is applied to analyzing 840 company information extracted from job-offering documents sent to Saga University. According to the preceding study, its essence is not just data size, but a ratio of map size to data size. Then, training performance of each SOM, whose size of the competitive layer is 35x35, 40x40, 45x45, 50x50, or 55x55, is investigated through some computer simulations. As a result, a smaller SOM cannot develop an appropriate feature map successfully, because the competitive layer is too small to develop the overall data set property. Moreover, a common winner for different applied samples is observed, the number of such common winners is quite big, and its distribution is not uniform. In contrast, a bigger SOM has an enough space to develop, so above-mentioned problems are solved.