A High-Dimensional Data Quality Metric using Pareto Optimality
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Date
2017
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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.
Description
@inproceedings{10.2312:eurp.20171187,
booktitle = {EuroVis 2017 - Posters},
editor = {Anna Puig Puig and Tobias Isenberg},
title = {{A High-Dimensional Data Quality Metric using Pareto Optimality}},
author = {Post, Tobias and Wischgoll, Thomas and Hamann, Bernd and Hagen, Hans},
year = {2017},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-044-4},
DOI = {10.2312/eurp.20171187}
}