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Issue No. 05 - May (2011 vol. 17)
ISSN: 1077-2626
pp: 584-597
Andrada Tatu , University of Konstanz, Konstanz
Georgia Albuquerque , TU Braunschweig, Braunschweig
Martin Eisemann , TU Braunschweig, Braunschweig
Peter Bak , University of Konstanz, Konstanz
Holger Theisel , University of Magdeburg, Magdeburg
Marcus Magnor , TU Braunschweig, Braunschweig
Daniel Keim , University of Konstanz, Konstanz
Visual exploration of multivariate data typically requires projection onto lower dimensional representations. The number of possible representations grows rapidly with the number of dimensions, and manual exploration quickly becomes ineffective or even unfeasible. This paper proposes automatic analysis methods to extract potentially relevant visual structures from a set of candidate visualizations. Based on features, the visualizations are ranked in accordance with a specified user task. The user is provided with a manageable number of potentially useful candidate visualizations, which can be used as a starting point for interactive data analysis. This can effectively ease the task of finding truly useful visualizations and potentially speed up the data exploration task. In this paper, we present ranking measures for class-based as well as non-class-based scatterplots and parallel coordinates visualizations. The proposed analysis methods are evaluated on different data sets.
Dimensionality reduction, quality measures, scatterplots, parallel coordinates.

M. Eisemann et al., "Automated Analytical Methods to Support Visual Exploration of High-Dimensional Data," in IEEE Transactions on Visualization & Computer Graphics, vol. 17, no. , pp. 584-597, 2010.
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