Authors:
Hoa Nguyen
1
and
Paul Rosen
2
Affiliations:
1
University of Utah, United States
;
2
University of South Florida, United States
Keyword(s):
Correlation, Correlation Visualization, Statistical Visualization.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
General Data Visualization
;
Information and Scientific Visualization
Abstract:
Correlation is a powerful relationship measure used in science, engineering, and business to estimate trends
and make forecasts. Visualization methods, such as scatterplots and parallel coordinates, are designed to be
general, supporting many visualization tasks, including identifying correlation. However, due to their generality,
they do not provide the most efficient interface, in terms of speed and accuracy. This can be problematic
when a task needs to be repeated frequently. To address this shortcoming, we propose a new correlation
task-specific visualization method called Correlation Coordinate Plots (CCPs). CCPs transform data into a
powerful coordinate system for estimating the direction and strength of correlation. To support multiple attributes,
we propose 2 additional interfaces. The first is the Snowflake Visualization, a focus+context layout
for exploring all pairwise correlations. The second enhances the basic CCP by using principal component
analysis to project multiple
attributes. We validate CCP performance in correlation-specific tasks through an
extensive user study that shows improvement in both accuracy and speed.
(More)