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Proceedings Paper

Dynamic statistical graphics techniques for exploring the structure of multivariate data
Author(s): Forrest W. Young; Penny L. Rheingans
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Paper Abstract

We present and discuss several Dynamic Statistical Graphics tools designed to help the data analyst visually discover and formulate hypotheses about the structure of multivariate data. All tools are based on the notion of the "data space", a representation of multivariate data as a high-dimensional space which has a dimension for each variable (column of the data) and a point for each case (row of the data). The data space is projected orthogonally onto the "visual space", a threedimensional space which is seen and manipulated by the data analyst. The visual space has a point-like object for each case and can have a vector-like object for each variable. The three dimensions of the visual space are orthogonal linear combinations of the variables. We discuss the notion of a "Guided Tour" of multivariate data space, and present guided tour tools. These tools include: I 6D-rotation, a tool for dynamically rotating, in six-dimensional (6D) space, from one 3D portion of the data space to another while displaying the dynamically changing projection in the visual-space; . hD-residualization, a tool that determines, at the user's request, the largest invisible 3D-space - i.e., the largest 3D space orthogonal to the visual space. This space is used with the visual space so that 6D-rotation can occur between two new 3D portions of the data space; I projection-cuing, a group of three tools that use change in object brightness as a cue to show change in aspects of the projection of objects from the data space to the visual space during hD-rotation. In addition to these tools for touring high-dimensional mu1tivariate space, we discuss tools for manipulating the 3D visual space, and a tool for looking at the relationship between two data spaces. Finally, we present a guided tour implementation in which the user manipulates joysticks and sliders to dynamically and smoothly

Paper Details

Date Published: 1 August 1990
PDF: 12 pages
Proc. SPIE 1259, Extracting Meaning from Complex Data: Processing, Display, Interaction, (1 August 1990); doi: 10.1117/12.19983
Show Author Affiliations
Forrest W. Young, Univ. of North Carolina (United States)
Penny L. Rheingans, Univ. of North Carolina (United States)

Published in SPIE Proceedings Vol. 1259:
Extracting Meaning from Complex Data: Processing, Display, Interaction
Edward J. Farrell, Editor(s)