Frontiers of Information Technology & Electronic Engineering
>> 2023,
Volume 24,
Issue 7
doi:
10.1631/FITEE.2200409
Explainable data transformation recommendation for automatic visualization
Affiliation(s): State Key Lab of CAD & CG, Zhejiang University, Hangzhou 310058, China; Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China; School of Computer Science and Engineering, Central South University, Changsha 410083, China; less
Received: 2022-09-23
Accepted: 2023-07-24
Available online: 2023-07-24
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Abstract
generates meaningful visualizations to support data analysis and pattern finding for novice or casual users who are not familiar with visualization design. Current approaches adopt mainly aggregation and filtering to extract patterns from the original data. However, these limited s fail to capture complex patterns such as clusters and correlations. Although recent advances in feature engineering provide the potential for more kinds of automatic s, the auto-generated transformations lack concerning how patterns are connected with the original features. To tackle these challenges, we propose a novel explainable recommendation approach for extended kinds of s in . We summarize the space of feasible s and measures on 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 . We demonstrate the effectiveness of our approach through two cases and a user study.
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