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dc.contributor.authorWang, Yunhaien_US
dc.contributor.authorLi, Jingtingen_US
dc.contributor.authorNie, Feipingen_US
dc.contributor.authorTheisel, Holgeren_US
dc.contributor.authorGong, Minglunen_US
dc.contributor.authorLehmann, Dirk J.en_US
dc.contributor.editorHeer, Jeffrey and Ropinski, Timo and van Wijk, Jarkeen_US
dc.description.abstractOne main task for domain experts in analysing their nD data is to detect and interpret class/cluster separations and outliers. In fact, an important question is, which features/dimensions separate classes best or allow a cluster-based data classification. Common approaches rely on projections from nD to 2D, which comes with some challenges, such as: The space of projection contains an infinite number of items. How to find the right one? The projection approaches suffers from distortions and misleading effects. How to rely to the projected class/cluster separation? The projections involve the complete set of dimensions/ features. How to identify irrelevant dimensions? Thus, to address these challenges, we introduce a visual analytics concept for the feature selection based on linear discriminative star coordinates (DSC), which generate optimal cluster separating views in a linear sense for both labeled and unlabeled data. This way the user is able to explore how each dimension contributes to clustering. To support to explore relations between clusters and data dimensions, we provide a set of cluster-aware interactions allowing to smartly iterate through subspaces of both records and features in a guided manner. We demonstrate our features selection approach for optimal cluster/class separation analysis with a couple of experiments on real-life benchmark high-dimensional data sets.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectI.3.3 [Computer Graphics]
dc.subjectPicture/Image Generation
dc.subjectLine and curve generation
dc.titleLinear Discriminative Star Coordinates for Exploring Class and Cluster Separation of High Dimensional Dataen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersMulti and High Dimensional Visualization

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  • 36-Issue 3
    EuroVis 2017 - Conference Proceedings

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