Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (1): 99-109.DOI: 10.3778/j.issn.1002-8331.2002-0194

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ASExplorer: Multi-dimensional Correlation Visual Analysis System Based on Joint Entropy

ZHANG Di, YANG Pei, DENG Xinbo, ZHAO Qianchuan   

  1. 1.School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China
    2.Liuying Team, Beijing Shuziguanxing Science and Technology Co., Ltd., Beijing 100080, China
    3.Deptartment of Automation, Tsinghua University, Beijing 100084, China
  • Online:2021-01-01 Published:2020-12-31



  1. 1.兰州理工大学 计算机与通信学院,兰州 730050
    2.北京数字观星科技有限公司 流影团队,北京 100080
    3.清华大学 自动化系,北京 100084


Multi-dimensional correlation analysis has always been the research emphasis in the field of data analysis. Traditional visualization can intuitively judge the type of correlation in several data dimensions by graphical method, but hard to solve the curse of dimensionality. Although some data mining methods are feasible, it’s hard to visualize the process, and still need parameter guidance supplied by visualization in many scenes. ASExplorer is a visual analytics system developed for exploring the relevance of data dimension. In the first place, it can help users choose analysis path and filter data by an algorithm of dimension importance evaluation based on joint entropy, then explorer the correlativity among multiple dimensions when sampling scale changes. This system is suitable for the early analysis of data set lacking prior knowledge. Case study and user research verify the effectiveness of the system.

Key words: high-dimensional data, joint entropy, data visualization, visual analysis



关键词: 高维数据, 联合熵, 数据可视化, 可视分析