Parallel Coordinates Based Visualization for High-Dimensional Data
Weiyu Li, Jiying Lang, He Zhang, Fei Yang, Lei Zhang, Jingchang Pan · 2019
High-dimensional data is generated in a wide range of scientific, social and economic life. Identifying and analyzing these high-dimensional data efficiently can improve the scientificity of decisions. Visualization can transform abstract data or large scale digital representation into an intuitive visible form, which will greatly enhance the insight of latent schema information for decision makers, and thus provide the basis for in-depth analysis and processing of high-dimensional data. This paper will explore a visualization framework to visualize the high-dimensional data through both visual angle and the intrinsic meaning of data. The rules behind data and information can thus clearly and intuitively be seen through data visualization.