Teaching:TUW - UE InfoVis WS 2010/11 - Gruppe 05 - Aufgabe 2: Difference between revisions
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== Metrics == | == Metrics == | ||
[[Image:Dasgupta+Kosara Pixel-Space Histograms.png|thumb|300px|right|Figure 1: Pixel-space histograms, see [Dasgupta and Kosara, 2010] p1018]] | |||
To automatically enhance the analytical tasks of users Dasgupta and Kosara [2010] proposed several metrics. | To automatically enhance the analytical tasks of users Dasgupta and Kosara [2010] proposed several metrics. | ||
The metrics are used to measure the properties of parallel coordinates. | The metrics are used to measure the properties of parallel coordinates. | ||
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Pixel-space histograms discretize the lines drawn in parallel coordinates into bins - each bin being one pixel (for a total of ''h'' pixels in an axis): | Pixel-space histograms discretize the lines drawn in parallel coordinates into bins - each bin being one pixel (for a total of ''h'' pixels in an axis): | ||
* One-Dimensional Axis Histogram | * One-Dimensional Axis Histogram | ||
*: A vector ''b'' containing the number of lines that start or end at this pixel. | *: A vector ''b'' containing the number of lines that start or end at this pixel - see columns A and B in figure 1. | ||
* One-Dimensional Distance Histogram | * One-Dimensional Distance Histogram | ||
*: A vector ''d'' where each component measures the slope of lines. | *: A vector ''d'' where each component measures the slope of lines. | ||
* Two-Dimensional Axis Pair Histogram | * Two-Dimensional Axis Pair Histogram | ||
*: A matrix where each cell ''x<sub>i,j</sub>'' means that ''n'' lines are going from pixel ''i'' to pixel ''j''. | *: A matrix where each cell ''x<sub>i,j</sub>'' means that ''n'' lines are going from pixel ''i'' to pixel ''j'' - see matrix in figure 1. | ||
=== Metrics ... === | === Metrics ... === | ||
Revision as of 23:32, 16 November 2010
Pargnostics: Screen-Space Metrics for Parallel Coordinates
Introduction
Visualization still takes place in a space with a limited number of discrete pixels. The result of this often is over-plotting, clutter or other things. Most of the time this structures are avoided. But sometimes this artifacts can be useful, because they might point out interesting structures in the data. Until know not a lot of attention is paid to the way visualzation is presented on the screen. For the case of parallel coordinates so called Pargnostics (Parallel coordinates diagnostics) should act as a bridge between the created visualization and the perceptual system of the user. Based on metrics it's possible to provide a optimization which maximize or minimize certain visual artifacts.
Metrics
To automatically enhance the analytical tasks of users Dasgupta and Kosara [2010] proposed several metrics. The metrics are used to measure the properties of parallel coordinates. For calculating these metrics, first pixel-space histograms need to be calculated. Pixel-space histograms discretize the lines drawn in parallel coordinates into bins - each bin being one pixel (for a total of h pixels in an axis):
- One-Dimensional Axis Histogram
- A vector b containing the number of lines that start or end at this pixel - see columns A and B in figure 1.
- One-Dimensional Distance Histogram
- A vector d where each component measures the slope of lines.
- Two-Dimensional Axis Pair Histogram
- A matrix where each cell xi,j means that n lines are going from pixel i to pixel j - see matrix in figure 1.
Metrics ...
- Number of Line Crossings
- Angles of Crossing
- Parallelism
- Mutual Information
- Convergence, Divergence
- Over-plotting
- Pixel-based Entropy
Dimension Order Optimization
Conclusion
References
[Dasgupta and Kosara, 2010] Aritra Dasgupta and Robert Kosara. Pargnostics: Screen-Space Metrics for Parallel Coordinates. IEEE Transactions on Visualization and Computer Graphics, 16(6):1017-1026, November/December 2010