Clutter-Based Dimension Reordering in Multi-Dimensional Data Visualization

Visual clutter denotes a disordered collection of graphical entities in information visualization. It can obscure the structure present in the data. Even in a small dataset, visual clutter makes it hard for the viewer to find patterns, relationships and structure. In this thesis, I study visual clutter with four distinct visualization techniques, and present the concept and framework of Clutter-Based Dimension Reordering (CBDR). Dimension order is an attribute that can significantly affect a visualization’s expressiveness. By varying the dimension order in a display, it is possible to reduce clutter without reducing data content or modifying the data in any way. Clutter reduction is a display-dependent task. In this thesis, I apply the CBDR framework to four different visualization techniques. For each display technique, I determine what constitutes clutter in terms of display properties, then design a metric to measure visual clutter in this display. Finally I search for an order that minimizes the clutter in a display. Different algorithms for the searching process are discussed in this thesis as well. In order to gather users’ responses toward the clutter measures used in the Clutter-Based Dimension Reordering process and validate the usefulness of CBDR, I also conducted an evaluation with two groups of users. The study result proves that users find our approach to be helpful for visually exploring datasets. The users also had many comments and suggestions for the CBDR approach as well as for visual clutter reduction in general. The content and result of the user study are included in this thesis.