Hierarchical parallel coordinates for exploration of large datasets
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
Sequential Operations in Digital Picture Processing
Journal of the ACM (JACM)
Digital Image Processing
Uncovering Clusters in Crowded Parallel Coordinates Visualizations
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Revealing Structure within Clustered Parallel Coordinates Displays
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
Give chance a chance: modeling density to enhance scatter plot quality through random data sampling
Information Visualization
Revealing structure in visualizations of dense 2D and 3D parallel coordinates
Information Visualization
Proceedings of the 2006 AVI workshop on BEyond time and errors: novel evaluation methods for information visualization
Outlier-Preserving Focus+Context Visualization in Parallel Coordinates
IEEE Transactions on Visualization and Computer Graphics
Measuring Data Abstraction Quality in Multiresolution Visualizations
IEEE Transactions on Visualization and Computer Graphics
A Taxonomy of Clutter Reduction for Information Visualisation
IEEE Transactions on Visualization and Computer Graphics
Splatting the lines in parallel coordinates
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
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The rendering of large data sets can result in cluttered displays and non-interactive update rates, leading to time consuming analyses. A straightforward solution is to reduce the number of items, thereby producing an abstraction of the data set. For the visual analysis to remain accurate, the graphical representation of the abstraction must preserve the significant features present in the original data. This paper presents a screen space quality method, based on distance transforms, that measures the visual quality of a data abstraction. This screen space measure is shown to better capture significant visual structures in data, compared with data space measures. The presented method is implemented on the GPU, allowing interactive creation of high quality graphical representations of multivariate data sets containing tens of thousands of items.