SepMe: 2002 New visual separation measures

Michael Aupetit, Michael Sedlmair

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2016 IEEE Pacific Visualization Symposium, PacificVis 2016 - Proceedings
PublisherIEEE Computer Society
Pages1-8
Number of pages8
Volume2016-May
ISBN (Electronic)9781509014514
DOIs
Publication statusPublished - 4 May 2016
Event9th IEEE Pacific Visualization Symposium, PacificVis 2016 - Taipei, Taiwan, Province of China
Duration: 19 Apr 201622 Apr 2016

Other

Other9th IEEE Pacific Visualization Symposium, PacificVis 2016
CountryTaiwan, Province of China
CityTaipei
Period19/4/1622/4/16

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Keywords

  • H.5.2 [Information Interfaces and Presentation]: User Interfaces - Theory and methods

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Software

Cite this

Aupetit, M., & Sedlmair, M. (2016). SepMe: 2002 New visual separation measures. In 2016 IEEE Pacific Visualization Symposium, PacificVis 2016 - Proceedings (Vol. 2016-May, pp. 1-8). [7465244] IEEE Computer Society. https://doi.org/10.1109/PACIFICVIS.2016.7465244