Abstract
We propose a three-step concept and visual design for supporting the visual exploration of high-dimensional data in scatterplots through eye-Tracking. First, we extract subsets in the underlying data using existing classifications, automated clustering algorithms, or eye-Tracking. For the latter, we map gaze to the underlying data dimensions in the scatterplot. Clusters of data points that have been the focus of the viewers' gaze are marked as clusters of interest (eye-mind hypothesis). In a second step, our concept extracts various properties from statistics and scagnostics from the clusters. The third step uses these measures to compare the current data clusters from the main scatterplot to the same data in other dimensions. The results enable analysts to retrieve similar or dissimilar views as guidance to explore the entire data set. We provide a proof-of-concept implementation as a test bench and describe a use case to show a practical application and initial results.
Original language | English |
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Title of host publication | Proceedings - ETRA 2022 |
Subtitle of host publication | ACM Symposium on Eye Tracking Research and Applications |
Editors | Stephen N. Spencer |
Publisher | ACM/IEEE |
Number of pages | 7 |
ISBN (Electronic) | 9781450392525 |
DOIs | |
Publication status | Published - 8 Jun 2022 |
Event | 2022 ACM Symposium on Eye Tracking Research and Applications: ETRA 2022 - Virtuell, United States Duration: 8 Jun 2022 → 11 Jun 2022 |
Conference
Conference | 2022 ACM Symposium on Eye Tracking Research and Applications |
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Abbreviated title | ETRA 2022 |
Country/Territory | United States |
City | Virtuell |
Period | 8/06/22 → 11/06/22 |
Keywords
- Eye tracking
- Implicit Data Selection
- Recommendation
- Scatter Plot Matrix
- Visual Analytics
ASJC Scopus subject areas
- Sensory Systems
- Human-Computer Interaction
- Ophthalmology
- Computer Vision and Pattern Recognition