ParSetgnostics: Quality Metrics for Parallel Sets

While there are many visualization techniques for exploring numeric data, only a few work with categorical data. One prominent example is Parallel Sets, showing data frequencies instead of data points ‐ analogous to parallel coordinates for numerical data. As nominal data does not have an intrinsic...

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Vydáno v:Computer graphics forum Ročník 40; číslo 3; s. 375 - 386
Hlavní autoři: Dennig, Frederik L., Fischer, Maximilian T., Blumenschein, Michael, Fuchs, Johannes, Keim, Daniel A., Dimara, Evanthia
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Blackwell Publishing Ltd 01.06.2021
Témata:
CCS Concepts
Clutter
Correlation analysis
Data points
Dimensional analysis
Human‐centered computing → Visualization design and evaluation methods
Mathematical analysis
Orthogonality
Reduction
ISSN:0167-7055, 1467-8659
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Shrnutí:While there are many visualization techniques for exploring numeric data, only a few work with categorical data. One prominent example is Parallel Sets, showing data frequencies instead of data points ‐ analogous to parallel coordinates for numerical data. As nominal data does not have an intrinsic order, the design of Parallel Sets is sensitive to visual clutter due to overlaps, crossings, and subdivision of ribbons hindering readability and pattern detection. In this paper, we propose a set of quality metrics, called ParSetgnostics (Parallel Sets diagnostics), which aim to improve Parallel Sets by reducing clutter. These quality metrics quantify important properties of Parallel Sets such as overlap, orthogonality, ribbon width variance, and mutual information to optimize the category and dimension ordering. By conducting a systematic correlation analysis between the individual metrics, we ensure their distinctiveness. Further, we evaluate the clutter reduction effect of ParSetgnostics by reconstructing six datasets from previous publications using Parallel Sets measuring and comparing their respective properties. Our results show that ParSetgostics facilitates multi‐dimensional analysis of categorical data by automatically providing optimized Parallel Set designs with a clutter reduction of up to 81% compared to the originally proposed Parallel Sets visualizations.
Bibliografie:ObjectType-Article-1
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.14314