Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering 论文

摘要

Clutter denotes a disordered collection of graphical entities in information visualization. Clutter can obscure the structure present in the data. Even in a small dataset, clutter can make it hard for the viewer to find patterns and reveal relationships. In this paper, we present the concept of clutter-based dimension reordering. Our hope is to reduce clutter without reducing information content or disturb data in any way. Dimension order is a variable that can significantly affect a visualization’s expressiveness. By varying the dimension order in visualizations, our goal is to find the views with the least amount of visual clutter. Clutter reduction is a display dependent task. We define different measures of what constitutes clutter in terms of display properties for four different visualization techniques. We then apply dimension ordering algorithms to search for a order that minimizes the clutter in a display.