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Proceedings Paper

DriftWeed: a visual metaphor for interactive analysis of multivariate data
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Paper Abstract

We present a visualization technique that allows a user to identify and detect patterns and structures within a multivariate data set. Our research builds on previous efforts to represent multivariate data in a 2D information display through the use of icon plots. Although the icon plot work done by Pickett and Brinstein is similar to our approach, we improve on their efforts in several ways. Our technique allows analysis of a time series without using animation; promotes visual differentiation of information clusters based on measures of variance; and facilitates exploration through direct manipulation of geometry based on scales of variance. Our goal is to provide a visualization that implicitly conveys the degree to which an elements ordered collection of attributes varies from the prevailing pattern of attributes for other elements in the collection. We apply this technique to multivariate abstract data nd use it to locate exceptional elements in a data set and divisions among clusters.

Paper Details

Date Published: 28 February 2000
PDF: 8 pages
Proc. SPIE 3960, Visual Data Exploration and Analysis VII, (28 February 2000); doi: 10.1117/12.378887
Show Author Affiliations
Stuart J. Rose, Univ. of Arizona and Pacific Northwest National Lab. (United States)
Pak Chung Wong, Pacific Northwest National Lab. (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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