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dc.contributor.authorZhao, Xunen_US
dc.contributor.authorCui, Weiweien_US
dc.contributor.authorWu, Yanhongen_US
dc.contributor.authorZhang, Haidongen_US
dc.contributor.authorQu, Huaminen_US
dc.contributor.authorZhang, Dongmeien_US
dc.contributor.editorGleicher, Michael and Viola, Ivan and Leitte, Heikeen_US
dc.description.abstractOutliers, the data instances that do not conform with normal patterns in a dataset, are widely studied in various domains, such as cybersecurity, social analysis, and public health. By detecting and analyzing outliers, users can either gain insights into abnormal patterns or purge the data of errors. However, different domains usually have different considerations with respect to outliers. Understanding the defining characteristics of outliers is essential for users to select and filter appropriate outliers based on their domain requirements. Unfortunately, most existing work focuses on the efficiency and accuracy of outlier detection, neglecting the importance of outlier interpretation. To address these issues, we propose Oui, a visual analytic system that helps users understand, interpret, and select the outliers detected by various algorithms. We also present a usage scenario on a real dataset and a qualitative user study to demonstrate the effectiveness and usefulness of our system.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectH.5.2 [Information Interfaces and Presentations]
dc.subjectUser Interfaces
dc.subjectGraphics user interfaces (GUI)
dc.titleOui! Outlier Interpretation on Multi-dimensional Data via Visual Analyticsen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersAnalysis Techniques

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  • 38-Issue 3
    EuroVis 2019 - Conference Proceedings

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