University of Konstanz, Germany.
IEEE Trans Vis Comput Graph. 2011 Dec;17(12):2203-12. doi: 10.1109/TVCG.2011.229.
In this paper, we present a systematization of techniques that use quality metrics to help in the visual exploration of meaningful patterns in high-dimensional data. In a number of recent papers, different quality metrics are proposed to automate the demanding search through large spaces of alternative visualizations (e.g., alternative projections or ordering), allowing the user to concentrate on the most promising visualizations suggested by the quality metrics. Over the last decade, this approach has witnessed a remarkable development but few reflections exist on how these methods are related to each other and how the approach can be developed further. For this purpose, we provide an overview of approaches that use quality metrics in high-dimensional data visualization and propose a systematization based on a thorough literature review. We carefully analyze the papers and derive a set of factors for discriminating the quality metrics, visualization techniques, and the process itself. The process is described through a reworked version of the well-known information visualization pipeline. We demonstrate the usefulness of our model by applying it to several existing approaches that use quality metrics, and we provide reflections on implications of our model for future research.
在本文中，我们系统地介绍了一些利用质量度量标准帮助可视化探索高维数据中有意义模式的技术。在最近的一些论文中，提出了不同的质量度量标准，以自动在大量替代可视化（例如，替代投影或排序）的空间中进行高要求的搜索，使用户可以专注于质量度量标准建议的最有前途的可视化。在过去十年中，这种方法取得了显著的发展，但对于这些方法如何相互关联以及如何进一步发展该方法，几乎没有任何反思。为此，我们提供了一个使用高维数据可视化中的质量度量标准的方法概述，并基于深入的文献回顾提出了一个系统化方法。我们仔细分析了这些论文，并得出了一组用于区分质量度量标准、可视化技术和过程本身的因素。该过程通过对著名的信息可视化管道的重新处理版本进行描述。我们通过将其应用于使用质量度量标准的几个现有方法来证明我们模型的有用性，并对我们模型对未来研究的影响进行了反思。
