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DimLift: Interactive Hierarchical Data Exploration through Dimensional Bundling

Garrison, Laura Ann; Müller, Juliane; Schreiber, Stefanie; Oeltze-Jafra, Steffen; Hauser, Helwig; Bruckner, Stefan
Journal article, Peer reviewed
Accepted version
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accepted version (6.261Mb)
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https://hdl.handle.net/11250/2992003
Utgivelsesdato
2021
Metadata
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  • Department of Informatics [748]
  • Registrations from Cristin [5511]
Originalversjon
IEEE Transactions on Visualization and Computer Graphics. 2021, 27 (6), 2908-2922.   10.1109/TVCG.2021.3057519
Sammendrag
The identification of interesting patterns and relationships is essential to exploratory data analysis. This becomes increasingly difficult in high dimensional datasets. While dimensionality reduction techniques can be utilized to reduce the analysis space, these may unintentionally bury key dimensions within a larger grouping and obfuscate meaningful patterns. With this work we introduce DimLift , a novel visual analysis method for creating and interacting with dimensional bundles . Generated through an iterative dimensionality reduction or user-driven approach, dimensional bundles are expressive groups of dimensions that contribute similarly to the variance of a dataset. Interactive exploration and reconstruction methods via a layered parallel coordinates plot allow users to lift interesting and subtle relationships to the surface, even in complex scenarios of missing and mixed data types. We exemplify the power of this technique in an expert case study on clinical cohort data alongside two additional case examples from nutrition and ecology.
Utgiver
IEEE
Tidsskrift
IEEE Transactions on Visualization and Computer Graphics
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Copyright 2021 IEEE

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