Liao Hongsen, Wu Yingcai, Chen Li, Chen Wei
IEEE Trans Vis Comput Graph. 2018 Sep;24(9):2531-2545. doi: 10.1109/TVCG.2017.2754480. Epub 2017 Sep 20.
The use of scatterplots is an important method for multivariate data visualization. The point distribution on the scatterplot, along with variable values represented by each point, can help analyze underlying patterns in data. However, determining the multivariate data variation on a scatterplot generated using projection methods, such as multidimensional scaling, is difficult. Furthermore, the point distribution becomes unclear when the data scale is large and clutter problems occur. These conditions can significantly decrease the usability of scatterplots on multivariate data analysis. In this study, we present a cluster-based visual abstraction method to enhance the visualization of multivariate scatterplots. Our method leverages an adapted multilabel clustering method to provide abstractions of high quality for scatterplots. An image-based method is used to deal with large scale data problem. Furthermore, a suite of glyphs is designed to visualize the data at different levels of detail and support data exploration. The view coordination between the glyph-based visualization and the table lens can effectively enhance the multivariate data analysis. Through numerical evaluations for data abstraction quality, case studies and a user study, we demonstrate the effectiveness and usability of the proposed techniques for multivariate data analysis on scatterplots.
散点图的使用是多变量数据可视化的一种重要方法。散点图上的点分布以及每个点所代表的变量值，有助于分析数据中的潜在模式。然而，确定使用投影方法（如多维缩放）生成的散点图上的多变量数据变化是困难的。此外，当数据规模较大且出现杂乱问题时，点分布会变得不清晰。这些情况会显著降低散点图在多变量数据分析中的可用性。在本研究中，我们提出一种基于聚类的视觉抽象方法来增强多变量散点图的可视化效果。我们的方法利用一种经过改进的多标签聚类方法为散点图提供高质量的抽象。一种基于图像的方法用于处理大规模数据问题。此外，还设计了一组图形符号来可视化不同详细程度的数据并支持数据探索。基于图形符号的可视化与表格透镜之间的视图协调可以有效地增强多变量数据分析。通过对数据抽象质量的数值评估、案例研究和用户研究，我们证明了所提出的技术在散点图多变量数据分析中的有效性和可用性。
