A Self-Organizing Method Using Data Movement on Spherical Surface

Abstract

The data visualization, which reduces data dimension to make us easy to see data directly, is important in data mining. It is important that one category becomes one cluster (we define this as data cohesion) and the clusters are standoff to each other (we define this as cluster separation) in data visualization. In this paper, we propose a self-organizing method using data movement on a spherical surface for data visualization. The proposed method puts all data points on the spherical surface and each data point moves on the spherical surface under the force from all the other data points. The key features of the proposed method are using a spherical surface as output space and employing weighted inter-point distance which emphasizes similarity between these data points. The experimental results show that the proposed method visualizes data with high data cohesion and high cluster separation by dint of above features.