ClustML: A Measure of Cluster Pattern Complexity in Scatterplots Learnt from Human-labeled Groupings

Authors. Mostafa Abbas, Ehsan Ullah, Abdelkader Baggag, Halima Bensmail, Michael Sedlmair, Michael Aupetit

Venue. (2024) Full Paper

Type. Full Paper

Materials. DOI link PDF

Abstract. Visual quality measures (VQMs) are designed to support analysts by automatically detecting and quantifying patterns in visualizations. We propose a new data-driven technique called ClustRank that allows to rank scatterplots according to visible grouping patterns. Our model first encodes scatterplots in the parametric space of a Gaussian Mixture Model, and then 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 determine the rank of the scatterplot. ClustRank improves on existing VQM techniques by mimicking human judgments on two-Gaussian cluster patterns and gives more accuracy when ranking general cluster patterns in scatterplots. We demonstrate its benefit by analyzing kinship data for genome-wide association studies, a domain in which experts rely on the visual analysis of large sets of scatterplots. We make the three benchmark datasets and the ClustRank VQM available for practical use and further improvements.

@article{abbas2024clustml,
    title        = {{ClustML}: A Measure of Cluster Pattern Complexity in Scatterplots Learnt from Human-labeled Groupings},
    author       = {Mostafa M. Abbas and Ehsan Ullah and Abdelkader Baggag and Halima Bensmail and Michael Sedlmair and Michael Aupetit},
    year         = {2024},
    journal      = {Information Visualization},
    publisher    = {{SAGE} Publications},
    volume       = {0},
    number       = {0},
    pages        = {14738716231220536},
    doi          = {10.1177/14738716231220536},
    url          = {https://doi.org/10.1177/14738716231220536},
    eprint       = {https://doi.org/10.1177/14738716231220536},
    abstract     = {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 quantify visual cluster patterns in scatterplots. 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.}
}