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Personalized Sketch-Based Brushing in Scatterplots

Fan, Chaoran; Hauser, Helwig
Journal article, Peer reviewed
Accepted version
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URI
https://hdl.handle.net/11250/2722776
Date
2019
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  • Department of Informatics [536]
  • Registrations from Cristin [65]
Original version
IEEE Computer Graphics and Applications. 2019, 39 (4), 28-39.   10.1109/MCG.2018.2881502
Abstract
Brushing is at the heart of most modern visual analytics solutions and effective and efficient brushing is crucial for successful interactive data exploration and analysis. As the user plays a central role in brushing, several data-driven brushing tools have been designed that are based on predicting the user's brushing goal. All of these general brushing models learn the users' average brushing preference, which is not optimal for every single user. In this paper, we propose an innovative framework that offers the user opportunities to improve the brushing technique while using it. We realized this framework with a CNN-based brushing technique and the result shows that with additional data from a particular user, the model can be refined (better performance in terms of accuracy), eventually converging to a personalized model based on a moderate amount of retraining.
Publisher
IEEE
Journal
IEEE Computer Graphics and Applications
Copyright
Copyright 2018 IEEE

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