@inproceedings{063fd1ab2f6a44bda74ecf6d695f6135,
title = "SepMe: 2002 New visual separation measures",
abstract = "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.",
keywords = "H.5.2 [Information Interfaces and Presentation]: User Interfaces - Theory and methods",
author = "Michael Aupetit and Michael Sedlmair",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 9th IEEE Pacific Visualization Symposium, PacificVis 2016 ; Conference date: 19-04-2016 Through 22-04-2016",
year = "2016",
month = may,
day = "4",
doi = "10.1109/PACIFICVIS.2016.7465244",
language = "English",
series = "IEEE Pacific Visualization Symposium",
publisher = "IEEE Computer Society",
pages = "1--8",
editor = "Chuck Hansen and Ivan Viola and Xiaoru Yuan",
booktitle = "2016 IEEE Pacific Visualization Symposium, PacificVis 2016 - Proceedings",
address = "United States",
}