Analyzing the Role of Dimension Arrangement for Data Visualization in Radviz

The Radial Coordinate Visualization (Radviz) technique has been widely used to effectively evaluate the existence of patterns in highly dimensional data sets. A crucial aspect of this technique lies in the arrangement of the dimensions, which determines the quality of the posterior visualization. Dimension arrangement (DA) has been shown to be an NP-problem and different heuristics have been proposed to solve it using optimization techniques. However, very little work has focused on understanding the relation between the arrangement of the dimensions and the quality of the visualization. In this paper we first present two variations of the DA problem: (1) a Radviz independent approach and (2) a Radviz dependent approach. We then describe the use of the Davies-Bouldin index to automatically evaluate the quality of a visualization i.e., its visual usefulness. Our empirical evaluation is extensive and uses both real and synthetic data sets in order to evaluate our proposed methods and to fully understand the impact that parameters such as number of samples, dimensions, or cluster separability have in the relation between the optimization algorithm and the visualization tool.