This paper contributes a novel visualization method, Missingness Glyph, for analysis and exploration of missing values in data. Missing values are a common challenge in most data generating domains and may cause a range of analysis issues. Missingness in data may indicate potential problems in data collection and pre-processing, or highlight important data characteristics. While the development and improvement of statistical methods for dealing with missing data is a research area in its own right, mainly focussing on replacing missing values with estimated values, considerably less focus has been put on visualization of missing values. Nonetheless, visualization and explorative analysis has great potential to support understanding of missingness in data, and to enable gaining of novel insights into patterns of missingness in a way that statistical methods are unable to. The Missingness Glyph supports identification of relevant missingness patterns in data, and is evaluated and compared to two other visualization methods in context of the missingness patterns. The results are promising and confirms that the Missingness Glyph in several cases perform better than the alternative visualization methods.

中文翻译：

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探索不存在的内容-基于字形的可视化分析缺失值

本文提供了一种新颖的可视化方法“缺失字形”，用于分析和探索数据中的缺失值。在大多数数据生成域中，缺少值是一个常见的挑战，并且可能导致一系列分析问题。数据丢失可能表明数据收集和预处理中可能存在问题，或者突出显示重要的数据特征。虽然开发和改进用于处理缺失数据的统计方法本身就是一个研究领域，但其主要重点是用估计值替换缺失值，但对缺失值的可视化的关注却大大减少。但是，可视化和探索性分析具有巨大的潜力，可以帮助理解数据的缺失，并以一种统计方法无法获得的方式，获得对失踪模式的新颖见解。缺失字形支持识别数据中的相关缺失模式，并在缺失模式的情况下进行评估并与其他两种可视化方法进行比较。结果令人鼓舞，并证实了缺失字形在某些情况下的性能要优于其他可视化方法。