Visual-assisted Outlier Preservation for Scatterplot Sampling

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dc.contributor.authorYang, Haiyanen_US
dc.contributor.authorPajarola, Renatoen_US
dc.contributor.editorGuthe, Michaelen_US
dc.contributor.editorGrosch, Thorstenen_US
dc.date.accessioned2023-09-25T11:38:06Z
dc.date.available2023-09-25T11:38:06Z
dc.date.issued2023
dc.description.abstractScatterplot sampling has long been an efficient and effective way to resolve the overplotting issues commonly occurring in large-scale scatterplot visualization applications. However, it is challenging to preserve the existence of low-density points or outliers after sampling for a sub-sampling algorithm if, at the same time, faithfully representing the relative data densities is of importance. In this work, we propose to address this issue in a visual-assisted manner. While the whole dataset is sub-sampled, the density of the outliers is modeled and visually integrated into the final scatterplot together with the sub-sampled point data. We showcase the effectiveness of our proposed method in various cases and user studies.en_US
dc.description.sectionheadersImage Visualization and Analysis
dc.description.seriesinformationVision, Modeling, and Visualization
dc.identifier.doi10.2312/vmv.20231233
dc.identifier.isbn978-3-03868-232-5
dc.identifier.pages115-121
dc.identifier.pages7 pages
dc.identifier.urihttps://doi.org/10.2312/vmv.20231233
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vmv20231233
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing → Information visualization; Visualization techniques
dc.subjectHuman
dc.subjectcentered computing → Information visualization
dc.subjectVisualization techniques
dc.titleVisual-assisted Outlier Preservation for Scatterplot Samplingen_US
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