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    Visualnostics: Visual Guidance Pictograms for Analyzing Projections of High-dimensional Data

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    Date
    2015
    Author
    Lehmann, Dirk J.
    Kemmler, Fritz
    Zhyhalava, Tatsiana
    Kirschke, Marco
    Theisel, Holger
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    Abstract
    The 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.
    BibTeX
    @article {10.1111:cgf.12641,
    journal = {Computer Graphics Forum},
    title = {{Visualnostics: Visual Guidance Pictograms for Analyzing Projections of High-dimensional Data}},
    author = {Lehmann, Dirk J. and Kemmler, Fritz and Zhyhalava, Tatsiana and Kirschke, Marco and Theisel, Holger},
    year = {2015},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    DOI = {10.1111/cgf.12641}
    }
    URI
    http://dx.doi.org/10.1111/cgf.12641
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    • 34-Issue 3
    • EuroVis15: Eurographics Conference on Visualization

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    System hosted at  Graz University of Technology.
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