Uncovering How Scatterplot Features Skew Visual Class Separation

要旨

Multi-class scatterplots are essential for visually comparing data, such as examining class distributions in dimensionality reduction and evaluating classification models. Visual class separation (VCS) measures quantify human perception but are largely derived from and evaluated with datasets reflecting limited types of scatterplot features (e.g., data distribution, similar class densities). Quantitatively identifying which scatterplot features are influential to VCS tasks can enable more robust guidance for future measures. We analyze the alignment between VCS measures and people's perceptions of class separation through a crowdsourced study using 70 scatterplot features relevant to class separation. To cover a wide range of scatterplot features, we generated a set of multi-class scatterplots from 6,947 real-world datasets. Our results highlight that multiple combinations of features are needed to best explain VCS. From our analysis, we develop a composite feature model that identifies key scatterplot features for measuring VCS task performance.

著者
S. Sandra Bae
University of Colorado Boulder, Boulder, Colorado, United States
Takanori Fujiwara
Linköping University, Norrköping, Sweden
Chin Tseng
University of North Carolina-Chapel Hill, Chapel Hill, North Carolina, United States
Danielle Albers. Szafir
University of North Carolina-Chapel Hill, Chapel Hill, North Carolina, United States
DOI

10.1145/3706598.3713976

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713976

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Make it Visible

G418+G419
6 件の発表
2025-04-30 23:10:00
2025-05-01 00:40:00
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