
SlopeDependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters
Parallel coordinates are a popular technique to visualize multidimensio...
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Pattern Recognition and Revealing using Parallel Coordinates Plot
Parallel coordinates plot (PCP) is an excellent tool for multivariate vi...
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Parallel Coordinate Order for HighDimensional Data
Visualization of highdimensional data is counterintuitive using conven...
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Bijective Deformations in R^n via Integral Curve Coordinates
We introduce Integral Curve Coordinates, which identify each point in a ...
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Inverse Diffusion Curves using Shape Optimization
The inverse diffusion curve problem focuses on automatic creation of dif...
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Visual MultiMetric Grouping of EyeTracking Data
We present an algorithmic and visual grouping of participants and eyetr...
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Analytic Definition of Curves and Surfaces by Parabolic Blending
A procedure for interpolating between specified points of a curve or sur...
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Evaluation of a Bundling Technique for Parallel Coordinates
We describe a technique for bundled curve representations in parallelcoordinates plots and present a controlled user study evaluating their effectiveness. Replacing the traditional C^0 polygonal lines by C^1 continuous piecewise Bezier curves makes it easier to visually trace data points through each coordinate axis. The resulting Bezier curves can then be bundled to visualize data with given cluster structures. Curve bundles are efficient to compute, provide visual separation between data clusters, reduce visual clutter, and present a clearer overview of the dataset. A controlled user study with 14 participants confirmed the effectiveness of curve bundling for parallelcoordinates visualization: 1) compared to polygonal lines, it is equally capable of revealing correlations between neighboring data attributes; 2) its geometric cues can be effective in displaying cluster information. For some datasets curve bundling allows the color perceptual channel to be applied to other data attributes, while for complex cluster patterns, bundling and color can represent clustering far more clearly than either alone.
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