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Proceedings Paper

Visualization of high-density 3D graphs using nonlinear visual space transformations
Author(s): Ming C. Hao; Umeshwar Dayal; Pankaj Garg; Vijay Machiraju
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Paper 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 out 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.

Paper Details

Date Published: 12 March 2002
PDF: 7 pages
Proc. SPIE 4665, Visualization and Data Analysis 2002, (12 March 2002); doi: 10.1117/12.458795
Show Author Affiliations
Ming C. Hao, Hewlett-Packard Labs. (United States)
Umeshwar Dayal, Hewlett-Packard Labs. (United States)
Pankaj Garg, Hewlett-Packard Labs. (United States)
Vijay Machiraju, Hewlett-Packard Labs. (United States)


Published in SPIE Proceedings Vol. 4665:
Visualization and Data Analysis 2002
Robert F. Erbacher; Philip C. Chen; Matti Groehn; Jonathan C. Roberts; Craig M. Wittenbrink, Editor(s)

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