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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| Veröffentlicht in: | Computer graphics forum Jg. 40; H. 3; S. 375 - 386 |
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| Format: | Journal Article |
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Blackwell Publishing Ltd
01.06.2021
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Blumenschein, Michael Fischer, Maximilian T. Dimara, Evanthia Dennig, Frederik L. Keim, Daniel A. Fuchs, Johannes |
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| Cites_doi | 10.1109/2945.722299 10.1145/502348.502357 10.1111/cgf.13446 10.1559/152304003100010929 10.1080/00949657508810123 10.1109/TVCG.2013.140 10.1145/3173574.3174209 10.1109/VAST.2006.261423 10.2307/1422498 10.1680/imotp.1898.19100 10.1109/TVCG.2016.2598589 10.1068/p200049 10.1109/TVCG.2006.76 10.1109/TVCG.2006.138 10.1109/TVCG.2012.23 10.1109/HRI.2016.7451825 10.1109/PacificVis.2014.43 10.1007/s001840000041 10.1109/INFVIS.2005.1532139 10.1109/TVCG.2016.2598467 10.1109/PACIFICVIS.2008.4475457 10.1109/INFVIS.2005.1532142 10.1057/palgrave.ivs.9500013.4 10.1007/978-1-4020-5614-7_2569 10.1109/INFVIS.2003.1249016 10.1002/j.1538-7305.1948.tb01338.x 10.1016/j.visinf.2019.03.006 10.1109/TVCG.2010.184 10.1111/cgf.14000 10.1109/VISUAL.2019.8933706 10.1007/bf01898350 10.1109/PacificVis.2018.00025 10.1111/cgf.12641 10.1037/h0043158 10.1145/956750.956837 10.1145/2684200.2684333 10.18653/v1/W19-4734 10.1109/IV.2011.32 |
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| SubjectTerms | CCS Concepts Clutter Correlation analysis Data points Dimensional analysis Human‐centered computing → Visualization design and evaluation methods Mathematical analysis Orthogonality Reduction |
| Title | ParSetgnostics: Quality Metrics for Parallel Sets |
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