Approximating scatterplots of large datasets using distribution splats

Matthew Camuto; Roger Crawfis; Barry G. Becker

doi:10.1117/12.378890

28 February 2000 Approximating scatterplots of large datasets using distribution splats

Matthew Camuto, Roger Crawfis, Barry G. Becker

Author Affiliations +

Proceedings Volume 3960, Visual Data Exploration and Analysis VII; (2000) https://doi.org/10.1117/12.378890
Event: Electronic Imaging, 2000, San Jose, CA, United States

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.

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Matthew Camuto, Matthew Camuto, Roger Crawfis, Roger Crawfis, Barry G. Becker, Barry G. Becker, } "Approximating scatterplots of large datasets using distribution splats", Proc. SPIE 3960, Visual Data Exploration and Analysis VII, (28 February 2000); doi: 10.1117/12.378890; https://doi.org/10.1117/12.378890

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