Flexible Linked Axes for Multivariate Data Visualization

Multivariate data visualization is a classic topic, for which many solutions have been proposed, each with its own strengths and weaknesses. In standard solutions the structure of the visualization is fixed, we explore how to give the user more freedom to define visualizations. Our new approach is b...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics Vol. 17; no. 12; pp. 2310 - 2316
Main Authors: Claessen, J. H. T., van Wijk, J. J.
Format: Journal Article
Language:English
Published: United States IEEE 01.12.2011
Subjects:
Data visualization
Histograms
Image color analysis
Multivariate data
Parallel Coordinates Plot
scatter plot
Scattering parameters
visualization
ISSN:1077-2626, 1941-0506, 1941-0506
Online Access:Get full text
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Summary:Multivariate data visualization is a classic topic, for which many solutions have been proposed, each with its own strengths and weaknesses. In standard solutions the structure of the visualization is fixed, we explore how to give the user more freedom to define visualizations. Our new approach is based on the usage of Flexible Linked Axes: The user is enabled to define a visualization by drawing and linking axes on a canvas. Each axis has an associated attribute and range, which can be adapted. Links between pairs of axes are used to show data in either scatter plot- or Parallel Coordinates Plot-style. Flexible Linked Axes enable users to define a wide variety of different visualizations. These include standard methods, such as scatter plot matrices, radar charts, and PCPs [11]; less well known approaches, such as Hyperboxes [1], TimeWheels [17], and many-to-many relational parallel coordinate displays [14]; and also custom visualizations, consisting of combinations of scatter plots and PCPs. Furthermore, our method allows users to define composite visualizations that automatically support brushing and linking. We have discussed our approach with ten prospective users, who found the concept easy to understand and highly promising.
Bibliography:ObjectType-Article-1
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ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2011.201