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dc.contributor.authorBernard, Jürgenen_US
dc.contributor.authorDobermann, Eduarden_US
dc.contributor.authorSedlmair, Michaelen_US
dc.contributor.authorFellner, Dieter W.en_US
dc.contributor.editorMichael Sedlmair and Christian Tominskien_US
dc.date.accessioned2017-06-12T05:16:24Z
dc.date.available2017-06-12T05:16:24Z
dc.date.issued2017
dc.identifier.isbn978-3-03868-042-0
dc.identifier.urihttp://dx.doi.org/10.2312/eurova.20171114
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/eurova20171114
dc.description.abstractCluster and outlier analysis are two important tasks. Due to their nature these tasks seem to be opposed to each other, i.e., data objects either belong to a cluster structure or a sparsely populated outlier region. In this work, we present a visual analytics tool that allows the combined analysis of clusters and outliers. Users can add multiple clustering and outlier analysis algorithms, compare results visually, and combine the algorithms' results. The usefulness of the combined analysis is demonstrated using the example of labeling unknown data sets. The usage scenario also shows that identified clusters and outliers can share joint areas of the data space.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectInformation systems
dc.subject
dc.subjectData mining
dc.subjectHuman
dc.subjectcentered computing
dc.subject
dc.subjectVisual analytics
dc.subjectInformation visualization
dc.subjectTheory of computation
dc.subject
dc.subjectActive learning
dc.subjectComputing methodologies
dc.subject
dc.subjectMachine learning
dc.titleCombining Cluster and Outlier Analysis with Visual Analyticsen_US
dc.description.seriesinformationEuroVis Workshop on Visual Analytics (EuroVA)
dc.description.sectionheadersSensemaking, Analytics, and Retrieval
dc.identifier.doi10.2312/eurova.20171114
dc.identifier.pages19-23


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