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Proceedings Paper

Approximating scatterplots of large datasets using distribution splats
Author(s): Matthew Camuto; Roger Crawfis; Barry G. Becker
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Paper Abstract

Many situations exist where the plotting of large data sets with categorical attributes is desired in a 3D coordinate system. For example, a marketing company may conduct a survey involving one million subjects and then plot peoples favorite car type against their weight, height and annual income. Scatter point plotting, in which each point is individually plotted at its correspond cartesian location using a defined primitive, is usually used to render a plot of this type. If the dependent variable is continuous, we can discretize the 3D space into bins or voxels and retain the average value of all records falling within each voxel. Previous work employed volume rendering techniques, in particular, splatting, to represent this aggregated data, by mapping each average value to a representative color.

Paper Details

Date Published: 28 February 2000
PDF: 11 pages
Proc. SPIE 3960, Visual Data Exploration and Analysis VII, (28 February 2000); doi: 10.1117/12.378890
Show Author Affiliations
Matthew Camuto, The Ohio State Univ. (United States)
Roger Crawfis, The Ohio State Univ. (United States)
Barry G. Becker, SGI (United States)

Published in SPIE Proceedings Vol. 3960:
Visual Data Exploration and Analysis VII
Robert F. Erbacher; Philip C. Chen; Jonathan C. Roberts; Craig M. Wittenbrink, Editor(s)

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