The sampling lens: making sense of saturated visualisations
CHI '05 Extended Abstracts on Human Factors in Computing Systems
Dust & magnet: multivariate information visualization using a magnet metaphor
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
Enabling Automatic Clutter Reduction in Parallel Coordinate Plots
IEEE Transactions on Visualization and Computer Graphics
Discussion: Interacting with parallel coordinates
Interacting with Computers
Mining Patterns for Visual Interpretation in a Multiple-Views Environment
Visual Data Mining
Creation and Collaboration: Engaging New Audiences for Information Visualization
Information Visualization
An interactive parallel coordinates technique applied to a tropical cyclone climate analysis
Computers & Geosciences
Situvis: A Visual Tool for Modeling a User's Behaviour Patterns in a Pervasive Environment
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
Judging correlation from scatterplots and parallel coordinate plots
Information Visualization
Journal of Visual Languages and Computing
Situvis: A sensor data analysis and abstraction tool for pervasive computing systems
Pervasive and Mobile Computing
Quantitative data visualization with interactive KDE surfaces
Proceedings of the 26th Spring Conference on Computer Graphics
Coaxial interactive viewer: a multi-dimensional data visualization with spatial distortional views
Proceedings of the 2011 Visual Information Communication - International Symposium
ACM Transactions on Intelligent Systems and Technology (TIST)
Conceptualizing Visual Uncertainty in Parallel Coordinates
Computer Graphics Forum
Tracing Tuples Across Dimensions: A Comparison of Scatterplots and Parallel Coordinate Plots
Computer Graphics Forum
Visualizing clusters in parallel coordinates for visual knowledge discovery
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
See what you know: analyzing data distribution to improve density map visualization
EUROVIS'07 Proceedings of the 9th Joint Eurographics / IEEE VGTC conference on Visualization
Multi-dimensional reduction and transfer function design using parallel coordinates
VG'10 Proceedings of the 8th IEEE/EG international conference on Volume Graphics
The shape coordinates system in visualization space
Proceedings of the 5th International Symposium on Visual Information Communication and Interaction
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
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
Structural decomposition trees
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Interactive bivariate mode trees for visual structure analysis
Proceedings of the 27th Spring Conference on Computer Graphics
Storygraph: extracting patterns from spatio-temporal data
Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics
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The one-to-one strategy of mapping each single data item into a graphical marker adopted in many visualization techniques has limited usefulness when the number of records and/or the dimensionality of the data set are very high. In this situation, the strong overlapping of graphical markers severely hampers the userýs ability to identify patterns in the data from its visual representation. We tackle this problem here with a strategy that computes frequency or density information from the data set, and uses such information in Parallel Coordinates visualizations to filter out the information to be presented to the user, thus reducing visual clutter and allowing the analyst to observe relevant patterns in the data. The algorithms to construct such visualizations, and the interaction mechanisms supported, inspired by traditional image processing techniques such as grayscale manipulation and thresholding are also presented. We also illustrate how such algorithms can assist users to effectively identify clusters in very noisy large data sets.