An optimal radial layout for high dimensional data class visualization

Multivariate data visualization is an interesting research field with many applications in ubiquitous fields of sciences. Radial visualization is one of the most common information visualization techniques for visualizing multivariate data. Unfortunately, Radial visualization display different information about structures of multivariate data on the different positions of dimensional anchors on the unit circle. In this paper, we propose a method that improve the Radviz layout for class visualization of high-dimensional data. We apply the differential evolution algorithm to find the optimal dimensional anchors of the RadViz such that maximum the quality of Radial visualization for classifier data. We use the k nearest neighbors classifier for quality measurement. Our method provides an improvement visualizing class structures of high-dimensional data sets on the RadViz. We demonstrate the efficiency of our method for some data sets.