A New Axes Re-ordering Method in Parallel Coordinates Visualization

Abstract

Visualization and interaction of multidimensional data always requires optimized solutions to integrate the display, exploration and analytical reasoning of data into one visual pipeline for human-centered data analysis and interpretation. Parallel coordinate, as one of the popular multidimensional data visualization techniques, is suffered from the visual clutter problem. Though changing the ordering of axis is a straightforward way to address it, optimizing the order of axis is a NP-complete problem. In this paper, we propose a new axes re-ordering method in parallel coordinates visualization: a similarity-based method, which is based on the combination of Nonlinear Correlation Coefficient (NCC) and Singular Value Decomposition (SVD) algorithms. By using this approach, the first remarkable axe can be selected based on mathematics theory and all axis are re-ordered in line with the degree of similarities among them. Meanwhile, we would also propose a measurement of contribution rate of each dimension to reveal the property hidden in the dataset. At last, case studies demonstrate the rationale and effectiveness of our approaches: NCC reordering method can enlarge the mean crossing angles and reduce the amount of polylines between the neighboring axes. It can reduce the computational complexity greatly in comparison with other re-ordering methods.