中文版 | English
Title

Explainable data transformation recommendation for automatic visualization

Author
Corresponding AuthorChen, Wei
Publication Years
2022-12-01
DOI
Source Title
ISSN
2095-9184
EISSN
2095-9230
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.
Keywords
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
Others
Funding Project
National Natural Science Foundation of China[62132017] ; Fundamental Research Fundsfor the Central Universities, China[226202200235]
WOS Research Area
Computer Science ; Engineering
WOS Subject
Computer Science, Information Systems ; Computer Science, Software Engineering ; Engineering, Electrical & Electronic
WOS Accession No
WOS:000903188700001
Publisher
Data Source
Web of Science
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/420774
DepartmentDepartment of Computer Science and Engineering
Affiliation
1.Zhejiang Univ, State Key Lab CAD & CG, Hangzhou 310058, Peoples R China
2.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China
3.Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, Peoples R China
Recommended Citation
GB/T 7714
Wu, Ziliang,Chen, Wei,Ma, Yuxin,et al. Explainable data transformation recommendation for automatic visualization[J]. Frontiers of Information Technology & Electronic Engineering,2022.
APA
Wu, Ziliang.,Chen, Wei.,Ma, Yuxin.,Xu, Tong.,Yan, Fan.,...&Xia, Jiazhi.(2022).Explainable data transformation recommendation for automatic visualization.Frontiers of Information Technology & Electronic Engineering.
MLA
Wu, Ziliang,et al."Explainable data transformation recommendation for automatic visualization".Frontiers of Information Technology & Electronic Engineering (2022).
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