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.
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.
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Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Ziliang WU designed the research and drafted the paper. Wei CHEN, Yuxin MA, and Jiazhi XIA helped organize the paper. Ziliang WU, Tong XU, and Lei LV implemented the system. Fan YAN and Zhonghao QIAN collected the data. Wei CHEN revised and finalized the paper.
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Ziliang WU, Wei CHEN, Yuxin MA, Tong XU, Fan YAN, Lei LV, Zhonghao QIAN, and Jiazhi XIA declare that they have no conflict of interest.
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Project supported by the National Natural Science Foundation of China (No. 62132017) and the Fundamental Research Funds for the Central Universities, China (No. 226202200235)
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Wu, Z., Chen, W., Ma, Y. et al. Explainable data transformation recommendation for automatic visualization. Front Inform Technol Electron Eng 24, 1007–1027 (2023). https://doi.org/10.1631/FITEE.2200409
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DOI: https://doi.org/10.1631/FITEE.2200409