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    • 39-Issue 1
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    • 39-Issue 1
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    Hinted Star Coordinates for Mixed Data

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    Date
    2020
    Author
    Matute, J.
    Linsen, L.
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    Abstract
    Mixed data sets containing numerical and categorical attributes are nowadays ubiquitous. Converting them to one attribute type may lead to a loss of information. We present an approach for handling numerical and categorical attributes in a holistic view. For data sets with many attributes, dimensionality reduction (DR) methods can help to generate visual representations involving all attributes. While automatic DR for mixed data sets is possible using weighted combinations, the impact of each attribute on the resulting projection is difficult to measure. Interactive support allows the user to understand the impact of data dimensions in the formation of patterns. Star Coordinates is a well‐known interactive linear DR technique for multi‐dimensional numerical data sets. We propose to extend Star Coordinates and its initial configuration schemes to mixed data sets. In conjunction with analysing numerical attributes, our extension allows for exploring the impact of categorical dimensions and individual categories on the structure of the entire data set. The main challenge when interacting with Star Coordinates is typically to find a good configuration of the attribute axes. We propose a guided mixed data analysis based on maximizing projection quality measures by the use of recommended transformations, named hints, in order to find a proper configuration of the attribute axes.
    BibTeX
    @article {10.1111:cgf.13666,
    journal = {Computer Graphics Forum},
    title = {{Hinted Star Coordinates for Mixed Data}},
    author = {Matute, J. and Linsen, L.},
    year = {2020},
    publisher = {© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.13666}
    }
    URI
    https://doi.org/10.1111/cgf.13666
    https://diglib.eg.org:443/handle/10.1111/cgf13666
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    Eurographics Association copyright © 2013 - 2020 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA