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dc.contributor.authorLehmann, Dirk J.en_US
dc.contributor.authorKemmler, Fritzen_US
dc.contributor.authorZhyhalava, Tatsianaen_US
dc.contributor.authorKirschke, Marcoen_US
dc.contributor.authorTheisel, Holgeren_US
dc.contributor.editorH. Carr, K.-L. Ma, and G. Santuccien_US
dc.date.accessioned2015-05-22T12:51:39Z
dc.date.available2015-05-22T12:51:39Z
dc.date.issued2015en_US
dc.identifier.urihttp://dx.doi.org/10.1111/cgf.12641en_US
dc.description.abstractThe visual analysis of multivariate projections is a challenging task, because complex visual structures occur. This causes fatigue or misinterpretations, which distorts the analysis. In fact, the same projection can lead to different analysis results. We provide visual guidance pictograms to improve objectivity of the visual search. A visual guidance pictogram is an iconic visual density map encoding the visual structure of certain data properties. By using them to guide the analysis, structures in the projection can be better understood and mentally linked to properties in the data. We introduce a systematic scheme for designing such pictograms and provide a set of pictograms for standard visual tasks, such as correlation and distribution analysis, for standard projections like scatterplots, RadVis, and Star Coordinates. We conduct a study that compares the visual analysis of real data with and without the support of guidance pictograms. Our tests show that the training effort for a visual search can be decreased and the analysis bias can be reduced by supporting the user's visual search with guidance pictograms.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectI.3.3 [Computer Graphics]en_US
dc.subjectPicture/Image Generationen_US
dc.subjectViewing algorithmsen_US
dc.titleVisualnostics: Visual Guidance Pictograms for Analyzing Projections of High-dimensional Dataen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.sectionheadersHigh-dimensional Visualizationen_US
dc.description.volume34en_US
dc.description.number3en_US
dc.identifier.doi10.1111/cgf.12641en_US
dc.identifier.pages291-300en_US


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