面向自动可视化的可解释数据变换推荐

吴子梁; 陈为; 马昱欣; 徐彤; 严凡; 吕檑; 钱中昊; 夏佳志

doi:10.1631/FITEE.2200409

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面向自动可视化的可解释数据变换推荐

常规文章 | Updated：2023-07-24

- 面向自动可视化的可解释数据变换推荐
- Explainable data transformation recommendation for automatic visualization
- 信息与电子工程前沿（英文） 2023年24卷第7期页码：1007-1027
- Affiliations：
  
  1.State Key Lab of CAD & CG, Zhejiang University, Hangzhou 310058, China
  2.Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
  3.School of Computer Science and Engineering, Central South University, Changsha 410083, China
- Author bio：
  
  †E-mail: wzlzju@zju.edu.cn
  ‡Corresponding author
- Funds：
  
  National Natural Science Foundation of China(62132017);Fundamental Research Funds for the Central Universities, China(226202200235)
- DOI：10.1631/FITEE.2200409
  中图分类号： TP391
- 收稿：2022-09-23，
  
  录用：2022-12-12，
  
  网络出版：2022-12-23，
  
  纸质出版：2023-07-0
- Accepted：
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吴子梁, 陈为, 马昱欣, 等. 面向自动可视化的可解释数据变换推荐[J]. 信息与电子工程前沿（英文）, 2023,24(7):1007-1027.

Ziliang WU, Wei CHEN, Yuxin MA, et al. Explainable data transformation recommendation for automatic visualization[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(7): 1007-1027.
吴子梁, 陈为, 马昱欣, 等. 面向自动可视化的可解释数据变换推荐[J]. 信息与电子工程前沿（英文）, 2023,24(7):1007-1027. DOI： 10.1631/FITEE.2200409.

Ziliang WU, Wei CHEN, Yuxin MA, et al. Explainable data transformation recommendation for automatic visualization[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(7): 1007-1027. DOI： 10.1631/FITEE.2200409.

摘要

自动可视化技术能够为不熟悉可视化设计的用户生成有意义的可视化，以支持他们的数据分析和模式发现需求。当前，主流的自动可视化方法采用聚合与过滤从原始数据抽取模式信息。然而，这些有限的数据变换并不能捕获聚类、关联等复杂的模式。尽管特征工程领域的最新进展为更加广泛的自动数据变换提供了可能，其结果却缺少可解释性，导致变换后的模式无法与原始数据特征建立联系。为应对上述挑战，我们面向自动可视化中广泛的数据变换类型，提出一种创新的可解释推荐方法。我们通过回顾既往文献总结可行的数据变换空间，通过开展预实验总结变换可解释性的度量。我们的推荐算法能够计算最优的数据变换，这种变换能够在维持可解释性的同时揭示数据的模式信息。真实场景下的使用案例与用户实验验证了我们方法的有效性。

Abstract

Automatic visualization generates meaningful visualizations to support data analysis and pattern finding for novice or casual users who are not familiar with visualization design. Current automatic visualization approaches adopt mainly aggregation and filtering to extract patterns from the original data. However

these limited data transformations fail to capture complex patterns such as clusters and correlations. Although recent advances in feature engineering provide the potential for more kinds of automatic data transformations

the auto-generated transformations lack explainability concerning how patterns are connected with the original features. To tackle these challenges

we propose a novel explainable recommendation approach for extended kinds of data transformations in automatic visualization. We summarize the space of feasible data transformations and measures on explainability of transformation operations with a literature review and a pilot study

respectively. A recommendation algorithm is designed to compute optimal transformations

which can reveal specified types of patterns and maintain explainability. We demonstrate the effectiveness of our approach through two cases and a user study.

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