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|a eng
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|a Anand, Anushka
|e verfasserin
|4 aut
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|a Automatic Selection of Partitioning Variables for Small Multiple Displays
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|c 2016
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|a Text
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|a Date Completed 05.02.2016
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|a Date Revised 04.11.2015
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a Effective small multiple displays are created by partitioning a visualization on variables that reveal interesting conditional structure in the data. We propose a method that automatically ranks partitioning variables, allowing analysts to focus on the most promising small multiple displays. Our approach is based on a randomized, non-parametric permutation test, which allows us to handle a wide range of quality measures for visual patterns defined on many different visualization types, while discounting spurious patterns. We demonstrate the effectiveness of our approach on scatterplots of real-world, multidimensional datasets
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|a Journal Article
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|a Talbot, Justin
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|t IEEE transactions on visualization and computer graphics
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|g 22(2016), 1 vom: 24. Jan., Seite 669-77
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