Another Look at Radial Visualization for Class-preserving Multivariate Data Visualization (Informatica, 2017)
Multivariate data visualization is an interesting research eld with many applications in various elds of sciences. Radial visualization is one of the most common information visualization concept for visualizing multivariate data. However, radial visualization may display different information about structures of multivariate data. For example, all points which are multiplicatives of given point may map to the same point in the visual space. An optimal layout of radial visualization is usually found by dening a suitable the order of data dimensions on the unit circle. In this paper, we propose a novel method that improves the radial visualization layout for cluster preservation of multivariate data. The traditional radial visualizations have viewpoint at the origin coordinate. The idea of our proposed method is nding the most suitable viewpoint among the corners of a hypercube to look into the cluster structures of data sets. Our method provides an improvement in visualizing class structures of multivariate data sets on the radial visualization. We present our method with three kinds of quality measurements and prove the effectiveness of our method for several data sets.