- Hyeon Jeon, Seoul Nationl University
- Ghulam Jilani Quadri, University of North Carolina, Chapel Hill
- Hyunwook Lee, UNIST
- Paul Rosen, University of Utah
- Danielle Albers Szafir, University of North Carolina, Chapel Hill
- Jinwook Seo, Seoul National University
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 way of perceiving clusters (i.e., conducting visual clustering) can differ due to the differences among individuals. Though such perceptual variability casts doubt on the reliability of data analysis based on visual clustering, we lack a systematic way to efficiently assess the variability. In this research, we study the 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 the widely used clustering techniques in predicting the 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.