Authors:
Gurminder Kaur
and
Bijaya B. Karki
Affiliation:
Louisiana State University, United States
Keyword(s):
Parallel Coordinates Plot, Multivariate Data, High Dimensions, Information Visualization, Focus + Context.
Related
Ontology
Subjects/Areas/Topics:
Abstract Data Visualization
;
Computer Vision, Visualization and Computer Graphics
;
General Data Visualization
;
High-Dimensional Data and Dimensionality Reduction
;
Information and Scientific Visualization
;
Large Data Visualization
;
Visual Data Analysis and Knowledge Discovery
;
Visualization Applications
Abstract:
Visualization of multivariate data using parallel coordinates plot (PCP) becomes overwhelming as the number of dimensions/variables increases beyond one dozen or so. Here we propose bifocal parallel coordinates plot (BPCP) based on the focus + context approach. BPCP splits vertically the overall rendering into the focus and context regions whose sizes can be adjusted to optimize the use of the available space. The focus area maps a few selected dimensions of interest, referred to as priority axes, at sufficiently wide spacing. The remaining dimensions are represented in the context area in a compact way so as to retain useful information and provide the data continuity. The focus display can be further enhanced with various options, such as axes overlays, scatterplot, and nested juxtaposed PCPs. In order to accommodate an arbitrarily large number of dimensions, the context display supports multi-level stacked view, each PCP level mapping a subset of the context axes. With flexible in
teractivity, BPCP can manage the priority axes and data rendering with respect to the corresponding dimensions to support exploratory visualization while providing useful context on the same visualization display.
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