ClustML: A Measure of Cluster Pattern Complexity in Scatterplots Learnt from Human-labeled Groupings
This provides a tool for analysts, such as in genome-wide association studies, to better assess scatterplot patterns, though it is incremental over existing visual quality measures.
The authors tackled the problem of automatically quantifying visual grouping patterns in scatterplots by proposing ClustML, a visual quality measure trained on human judgments, which improved estimation of human judgments on two-Gaussian cluster patterns and achieved higher accuracy in ranking general cluster patterns.
Visual quality measures (VQMs) are designed to support analysts by automatically detecting and quantifying patterns in visualizations. We propose a new VQM for visual grouping patterns in scatterplots, called ClustML, which is trained on previously collected human subject judgments. Our model encodes scatterplots in the parametric space of a Gaussian Mixture Model and uses a classifier trained on human judgment data to estimate the perceptual complexity of grouping patterns. The numbers of initial mixture components and final combined groups. It improves on existing VQMs, first, by better estimating human judgments on two-Gaussian cluster patterns and, second, by giving higher accuracy when ranking general cluster patterns in scatterplots. We use it to analyze kinship data for genome-wide association studies, in which experts rely on the visual analysis of large sets of scatterplots. We make the benchmark datasets and the new VQM available for practical use and further improvements.