Research Area: Machine Learning
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.
Keywords:
Data transformation
Data transformation recommendation
Automatic visualization
Explainability
Author(s) Name: Ziliang Wu, Wei Chen, Yuxin Ma, Tong Xu
Journal name: Frontiers of Information Technology & Electronic Engineering
Conferrence name:
Publisher name: Springer
DOI: 10.1631/FITEE.2200409
Volume Information: 24, pages 1007–1027 (2023)
