2016 IEEE Pacific Visualization Symposium (PacificVis) (2016)
April 19, 2016 to April 22, 2016
Michael Aupetit , Qatar Computing Research Institute, HBKU
Michael Sedlmair , University of Vienna
Our goal is to accurately model human class separation judgements in color-coded scatterplots. Towards this goal, we propose a set of 2002 visual separation measures, by systematically combining 17 neighborhood graphs and 14 class purity functions, with different parameterizations. Using a Machine Learning framework, we evaluate these measures based on how well they predict human separation judgements. We found that more than 58% of the 2002 new measures outperform the best state-of-the-art Distance Consistency (DSC) measure. Among the 2002, the best measure is the average proportion of same-class neighbors among the 0.35-Observable Neighbors of each point of the target class (short GONG 0.35 DIR CPT), with a prediction accuracy of 92.9%, which is 11.7% better than DSC. We also discuss alternative, well-performing measures and give guidelines when to use which.
H.5.2 [Information Interfaces and Presentation]: User Interfaces ? Theory and methods,
M. Aupetit and M. Sedlmair, "SepMe: 2002 New visual separation measures," 2016 IEEE Pacific Visualization Symposium (PacificVis)(PACIFICVIS), Taipei, Taiwan, 2016, pp. 1-8.