A High-Dimensional Data Quality Metric using Pareto Optimality

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dc.contributor.authorPost, Tobiasen_US
dc.contributor.authorWischgoll, Thomasen_US
dc.contributor.authorHamann, Bernden_US
dc.contributor.authorHagen, Hansen_US
dc.contributor.editorAnna Puig Puig and Tobias Isenbergen_US
dc.date.accessioned2017-06-12T05:18:09Z
dc.date.available2017-06-12T05:18:09Z
dc.date.issued2017
dc.description.abstractThe 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.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationEuroVis 2017 - Posters
dc.identifier.doi10.2312/eurp.20171187
dc.identifier.isbn978-3-03868-044-4
dc.identifier.pages133-135
dc.identifier.urihttps://doi.org/10.2312/eurp.20171187
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/eurp20171187
dc.publisherThe Eurographics Associationen_US
dc.titleA High-Dimensional Data Quality Metric using Pareto Optimalityen_US
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