Multiresolution multidimensional wavelet brushing
Proceedings of the 7th conference on Visualization '96
Introduction to volume rendering
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Hierarchical parallel coordinates for exploration of large datasets
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
Level-of-detail volume rendering via 3D textures
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Digital image analysis
Visualization Techniques for Mining Large Databases: A Comparison
IEEE Transactions on Knowledge and Data Engineering
INFOVIS '97 Proceedings of the 1997 IEEE Symposium on Information Visualization (InfoVis '97)
Interactive Information Visualization of a Million Items
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Revealing Structure within Clustered Parallel Coordinates Displays
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
Parallel Sets: Interactive Exploration and Visual Analysis of Categorical Data
IEEE Transactions on Visualization and Computer Graphics
A Taxonomy of Clutter Reduction for Information Visualisation
IEEE Transactions on Visualization and Computer Graphics
High performance multivariate visual data exploration for extremely large data
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Theoretical Foundations of Information Visualization
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An interactive parallel coordinates technique applied to a tropical cyclone climate analysis
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Online evolutionary context-aware classifier ensemble framework for object recognition
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Judging correlation from scatterplots and parallel coordinate plots
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Proceedings of the International Conference on Advanced Visual Interfaces
Quantitative data visualization with interactive KDE surfaces
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Coaxial interactive viewer: a multi-dimensional data visualization with spatial distortional views
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InfoShape: high-level views of multidimensional information
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Guided visualization of ultrasound image sequences
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Visualization for the Physical Sciences
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Illustrative parallel coordinates
EuroVis'08 Proceedings of the 10th Joint Eurographics / IEEE - VGTC conference on Visualization
A screen space quality method for data abstraction
EuroVis'08 Proceedings of the 10th Joint Eurographics / IEEE - VGTC conference on Visualization
Visual clustering in parallel coordinates
EuroVis'08 Proceedings of the 10th Joint Eurographics / IEEE - VGTC conference on Visualization
Evaluation of cluster identification performance for different PCP variants
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
Splatting the lines in parallel coordinates
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Visual coherence for large-scale line-plot visualizations
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Visualization of time-series data in parameter space for understanding facial dynamics
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
imMens: real-time visual querying of big data
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Focus+context visualization integrates a visually accentuated representation of selected data items in focus (more details, more opacity, etc.) with a visually deemphasized representation of the rest of the data, i.e., the context. The role of context visualization is to provide an overview of the data for improved user orientation and improved navigation. A good overview comprises the representation of both outliers and trends. Up to now, however, context visualization not really treated outliers sufficiently. In this paper we present a new approach to focus+context visualization in parallel coordinates which is truthful to outliers in the sense that small-scale features are detected before visualization and then treated specially during context visualization. Generally, we present a solution which enables context visualization at several levels of abstraction, both for the representation of outliers and trends. We introduce outlier detection and context generation to parallel coordinates on the basis of a binned data representation. This leads to an output-oriented visualization approach which means that only those parts of the visualization process are executed which actually affect the final rendering. Accordingly, the performance of this solution is much more dependent on the visualization size than on the data size which makes it especially interesting for large datasets. Previous approaches are outperformed, the new solution was successfully applied to datasets with up to 3 million data records and up to 50 dimensions.