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Proceedings Paper

Visualization and exploration of spatial probability density functions: a clustering-based approach
Author(s): Udeepta D. Bordoloi; David L. Kao; Han-Wei Shen
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Paper Abstract

We present an interactive visualization technique for spatial probability density function data. These are datasets that represent a spatial collection of random variables, and contain a number of possible outcomes for each random variable. It is impractical to visualize all the information at each spatial location as it will quickly lead to a cluttered image. We advocate the use of hierarchical clustering as a means of summarizing the information, and also as a tool to bring out meaningful spatial structures in the datasets. For clustering, we discuss a distance function which preserves the spatial correlation present in these datasets. To create an informative visualization of the clusters, we introduce a scheme of colors and patterns to represent statistical properties of the clusters.

Paper Details

Date Published: 4 June 2004
PDF: 8 pages
Proc. SPIE 5295, Visualization and Data Analysis 2004, (4 June 2004); doi: 10.1117/12.539250
Show Author Affiliations
Udeepta D. Bordoloi, The Ohio State Univ. (United States)
David L. Kao, NASA Ames Research Ctr. (United States)
Han-Wei Shen, The Ohio State Univ. (United States)

Published in SPIE Proceedings Vol. 5295:
Visualization and Data Analysis 2004
Robert F. Erbacher; Philip C. Chen; Jonathan C. Roberts; Matti T. Gröhn; Katy Börner, Editor(s)

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