Machine Learning in Information Visualization: Using Rule-based Learning Algorithms to Personalize Interfaces
| Autor: | Fátima L. S. Nunes, Ricardo Nakamura, Leonardo Souza Silva |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Association rule learning
business.industry Process (engineering) Computer science Interface (computing) Rule-based system Machine learning computer.software_genre Visualization Information visualization Resource (project management) User experience design Artificial intelligence business computer Algorithm |
| Zdroj: | IWSSIP |
| Popis: | Machine Learning has offered great potential in areas such as data mining and information retrieval, while less attention has been given to its use in the design of Information Visualization tools. Based on user needs, a properly designed interface can enhance augmented perception; however, the project is usually conducted without the inclusion of users. Machine Learning techniques can be useful to promote the users' involvement in the interface development process, contributing to offer personalized visualizations. Based on a dataset from a User Experience experiment with an interactive 3D environment to visualize medical temporal data, we present an approach using rule-based learning algorithms to promote the user's involvement. Our results indicate that Association Rule Learning can be a valuable resource to improve the design of Information Visualization interface tools as well as to customize visualization according to the user's preferences. |
| Databáze: | OpenAIRE |
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