In this paper, we examine the robustness of scagnostics through a series of theoretical and empirical studies. First, we investigate the sensitivity of scagnostics by employing perturbing operations on more than 60M synthetic and real-world scatterplots. We found that two scagnostic measures, Outlying and Clumpy, are overly sensitive to data binning. To understand how these measures align with human judgments of visual features, we conducted a study with 24 participants, which reveals that i) humans are not sensitive to small perturbations of the data that cause large changes in both measures, and ii) the perception of clumpiness heavily depends on per-cluster topologies and structures. Motivated by these results, we propose Robust Scagnostics (RScag) by combining adaptive binning with a hierarchy-based form of scagnostics. An analysis shows that RScag improves on the robustness of original scagnostics, aligns better with human judgments, and is equally fast as the traditional scagnostic measures.

中文翻译：

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提高怀疑论者的鲁棒性。

在本文中，我们通过一系列理论和实证研究检验了诊断学的鲁棒性。首先，我们通过对超过60M的合成散点图和实际散点图进行扰动操作，研究了诊断学的敏感性。我们发现，两个离群值检测方法：Outlying和Clumpy对数据分箱过于敏感。为了了解这些量度如何与人类对视觉特征的判断相吻合，我们与24名参与者进行了一项研究，该研究表明，i）人类对导致这两种量度发生较大变化的数据的微小扰动不敏感，并且ii）团块严重取决于每个集群的拓扑和结构。受这些结果的启发，我们提出了鲁棒的诊断学（RScag），方法是将自适应分级与基于层次结构的诊断学相结合。