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.

中文翻译：

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ParSetgnostics：并行集的质量度量

虽然有许多可视化技术可用于探索数值数据，但只有少数技术适用于分类数据。一个突出的例子是平行集，显示数据频率而不是数据点 - 类似于数值数据的平行坐标。由于标称数据没有内在顺序，Parallel Sets 的设计对视觉混乱很敏感，因为重叠、交叉和细分色带会阻碍可读性和模式检测。在本文中，我们提出了一组质量指标，称为 ParSetgnostics（并行集诊断），旨在通过减少混乱来改进并行集。这些质量指标量化了并行集的重要属性，例如重叠、正交性、色带宽度差异和互信息，以优化类别和维度排序。通过在各个指标之间进行系统的相关分析，我们确保了它们的独特性。此外，我们通过使用 Parallel Sets 测量和比较它们各自的属性重建先前出版物中的六个数据集来评估 ParSetgnostics 的杂波减少效果。我们的结果表明，与最初提出的并行集可视化相比，ParSetgostics 通过自动提供优化的并行集设计来促进分类数据的多维分析，杂波减少高达 81%。