  Visual clustering is a common perceptual task in scatterplots that supports
diverse analytics tasks (e.g., cluster identification). However, even with the
same scatterplot, the ways of perceiving clusters (i.e., conducting visual
clustering) can differ due to the differences among individuals and ambiguous
cluster boundaries. Although such perceptual variability casts doubt on the
reliability of data analysis based on visual clustering, we lack a systematic
way to efficiently assess this variability. In this research, we study
perceptual variability in conducting visual clustering, which we call Cluster
Ambiguity. To this end, we introduce CLAMS, a data-driven visual quality
measure for automatically predicting cluster ambiguity in monochrome
scatterplots. We first conduct a qualitative study to identify key factors that
affect the visual separation of clusters (e.g., proximity or size difference
between clusters). Based on study findings, we deploy a regression module that
estimates the human-judged separability of two clusters. Then, CLAMS predicts
cluster ambiguity by analyzing the aggregated results of all pairwise
separability between clusters that are generated by the module. CLAMS
outperforms widely-used clustering techniques in predicting ground truth
cluster ambiguity. Meanwhile, CLAMS exhibits performance on par with human
annotators. We conclude our work by presenting two applications for optimizing
and benchmarking data mining techniques using CLAMS. The interactive demo of
CLAMS is available at clusterambiguity.dev.
