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Article
A High-Dimensional Data Quality Metric using Pareto Optimality
Eurographics Conference on Visualization (EuroVis), Posters Track (2017)
  • Tobias Post
  • Thomas Wischgoll, Wright State University - Main Campus
  • Bernd Hamann
  • Hans Hagen
Document Type
Conference Proceeding
Publication Date
1-1-2017
Disciplines
Abstract

The representation of data quality within established high-dimensional data visualization techniques such as scatterplots and parallel coordinates is still an open problem. This work offers a scale-invariant measure based on Pareto optimality that is able to indicate the quality of data points with respect to the Pareto front. In cases where datasets contain noise or parameters that cannot easily be expressed or evaluated mathematically, the presented measure provides a visual encoding of the environment of a Pareto front to enable an enhanced visual inspection.

Comments

Presented at EuroVis 2017 - Posters.

DOI
10.2312/eurp.20171187
Citation Information
Tobias Post, Thomas Wischgoll, Bernd Hamann and Hans Hagen. "A High-Dimensional Data Quality Metric using Pareto Optimality" Eurographics Conference on Visualization (EuroVis), Posters Track (2017) (2017)
Available at: http://works.bepress.com/thomas_wischgoll/79/