ISBN: 978-981-14-4787-7 DOI: 10.18178/wcse.2020.06.002
A Visualization Recommendation Approach based on Machine Learning
Abstract— As an important means of data analysis, data visualization is used by more and more people. For most people who don't have visualization technology expertise, data visualization has some Visualization recommendation aims to lower the barrier to exploring basic visualizations by automatically generating results for analysts to search and select.This paper proposes a visual recommendation method based on machine learning, which can learn the most meaningful visualization results from many visualization practice datasets and mark them.Firstly, 22 data features and corresponding meaningful visualization types are extracted from 30 real visualization datasets. Then, binary classifiers are used to train the classification model, from which we can learn meaningful visualization and use crowdsourced testsets to test the accuracy.Finally, the results of multiple classifiers are fused to vote for multiple meaningful charts in the datasets.Experiments show that this method can effectively learn the meaningful visualization types in datasets, mark and recommend them to users.
Index Terms— visualization recommendation, machine Learning, classification model
Shichao Wei, Xin Li , Peiyin Song, Xiaofeng Zhou, Yichi Zhang, Shuai Li
Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang,CHINA
Key Laboratory of Network Control System, Chinese Academy of Sciences, Shenyang, CHINA
Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences,CHINA
Cite: Shichao Wei, Xin Li , Peiyin Song, Xiaofeng Zhou, Yichi Zhang, Shuai Li, "A Visualization Recommendation Approach based on Machine Learning " Proceedings of 2020 the 10th International Workshop on Computer Science and Engineering (WCSE 2020), pp. 6-12, Shanghai, China, 19-21 June, 2020.