IEEE Trans Vis Comput Graph. 2018 Jun;24(6):1997-2010. doi: 10.1109/TVCG.2017.2698041. Epub 2017 Apr 25.
We address the problem of visualizing multivariate correlations in parallel coordinates. We focus on multivariate correlation in the form of linear relationships between multiple variables. Traditional parallel coordinates are well prepared to show negative correlations between two attributes by distinct visual patterns. However, it is difficult to recognize positive correlations in parallel coordinates. Furthermore, there is no support to highlight multivariate correlations in parallel coordinates. In this paper, we exploit the indexed point representation of p -flats (planes in multidimensional data) to visualize local multivariate correlations in parallel coordinates. Our method yields clear visual signatures for negative and positive correlations alike, and it supports large datasets. All information is shown in a unified parallel coordinates framework, which leads to easy and familiar user interactions for analysts who have experience with traditional parallel coordinates. The usefulness of our method is demonstrated through examples of typical multidimensional datasets.
我们解决了在平行坐标中可视化多元相关关系的问题。我们关注多元相关关系，其形式为多个变量之间的线性关系。传统的平行坐标非常适合通过独特的视觉模式显示两个属性之间的负相关关系。然而，在平行坐标中很难识别正相关关系。此外，平行坐标中没有支持突出显示多元相关关系的功能。在本文中，我们利用 p-平面（多维数据中的平面）的索引点表示来在平行坐标中可视化局部多元相关关系。我们的方法为负相关和正相关都产生了清晰的视觉特征，并且支持大型数据集。所有信息都显示在统一的平行坐标框架中，这使得具有传统平行坐标经验的分析师可以轻松进行熟悉的用户交互。通过典型多维数据集的示例，展示了我们方法的有用性。
