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Visualization of High-Density 3D Graphs Using Non- Linear Visual Space Transformations

Hao, Ming C.; Dayal, Umeshwar; Garg, Pankaj; Machiraju, Vijay


Keyword(s): visualization; non-linear 3D space transformation; high-density

Abstract: The real world data distribution is seldom uniform. Clutter and sparsity commonly occur in visualization. Often, clutter results in overplotting, in which certain data items are not visible because other data items occlude them. Sparsity results in the inefficient use of the available display space. Common mechanisms to overcome this include reducing the amount of information displayed or using multiple representations with a varying amount of detail. This paper describes our experiments on "Non-Linear Visual Space Transformations" (NLVST). NLVST encompasses several innovative techniques: (1) employing a histogram for calculating the density of data distribution; (2) mapping the raw data values to a non-linear scale for stretching a high-density area; (3) tightening the sparse area to save the display space; (4) employing different color ranges of values on a non-linear scale according to the local density. We have applied NLVST to several web applications: market basket analysis, transactions observation, and IT search behavior analysis. Notes: Copyright SPIE. Published in and presented at the SPIE Conference on Visualization and Data Analysis, 20-25 January 2002, San Jose, CA

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