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Pargnostics: Screen-Space Metrics for Parallel Coordinates
November/December 2010 (vol. 16 no. 6)
pp. 1017-1026
Aritra Dasgupta, University of North Carolina at Charlotte
Robert Kosara, University of North Carolina at Charlotte
Interactive visualization requires the translation of data into a screen space of limited resolution. While currently ignored by most visualization models, this translation entails a loss of information and the introduction of a number of artifacts that can be useful, (e.g., aggregation, structures) or distracting (e.g., over-plotting, clutter) for the analysis. This phenomenon is observed in parallel coordinates, where overlapping lines between adjacent axes form distinct patterns, representing the relation between variables they connect. However, even for a small number of dimensions, the challenge is to effectively convey the relationships for all combinations of dimensions. The size of the dataset and a large number of dimensions only add to the complexity of this problem. To address these issues, we propose Pargnostics, parallel coordinates diagnostics, a model based on screen-space metrics that quantify the different visual structures. Pargnostics metrics are calculated for pairs of axes and take into account the resolution of the display as well as potential axis inversions. Metrics include the number of line crossings, crossing angles, convergence, overplotting, etc. To construct a visualization view, the user can pick from a ranked display showing pairs of coordinate axes and the structures between them, or examine all possible combinations of axes at once in a matrix display. Picking the best axes layout is an NP-complete problem in general, but we provide a way of automatically optimizing the display according to the user’s preferences based on our metrics and model.

[1] J. Allen, Maintaining knowledge about temporal intervals. Communications of the ACM, 26: 832–843, 1983.
[2] R. Amar, J. Eagan, and J. Stasko, Low-level components of analytic activity in information visualization. IEEE Symposium on Information Visualization, 2005. INFOVIS 2005., pages 111–117, 2004.
[3] G. Andrienko and N. Andrienko, Constructing parallel coordinates plot for problem solving. 1st International Symposium on, pp:9–14, 2001.
[4] M. Ankerst, S. Berchtold, and D. Keim, Similarity clustering of dimensions for an enhanced visualization of multidimensional data. In Proceedings Information Visualization, page 52. IEEE CS Press, 1998.
[5] E. Bertini and G. Santucci, By chance is not enough: Preserving relative density through non uniform sampling. Information Visualisation, International Conference on, 2004.
[6] E. Bertini and G. Santucci, Visual quality metrics. In BELIV '06: Proceedings of the 2006 AVI workshop on BEyond time and errors, pages 1–5, New York, NY, USA, 2006. ACM.
[7] M. Chen, D. Ebert, H. Hagen, R. Laramee, R. Van Liere, K. Ma, W. Ribarsky, G. Scheuermann, and D. Silver, Data, Information, and Knowledge in Visualization. IEEE Computer Graphics and Applications, 29 (1): 12–19, 2009.
[8] T. Cover and J. Thomas, Elements of information theory. Wiley, 2006.
[9] G. Ellis and A. Dix, Enabling automatic clutter reduction in parallel coordinate plots. IEEE Transactions on Visualization and Computer Graphics, 12 (5): 717–724, 2006.
[10] C. Forsell and J. Johansson, Task-based evaluation of multirelational 3D and standard 2D parallel coordinates. Proceedings of SPIE, pages 64950C–64950C–12, 2007.
[11] W. Huang, S.-H. Hong, and P. Eades, Effects of Crossing Angles. In Proceedings Pacific Visualization Symposium, pages 41–46. IEEE CS Press, 2008.
[12] A. Inselberg, Multidimensional detective. In Proceedings Visualization, pages 100–107. IEEE CS Press, 1997.
[13] A. Inselberg, Parallel Coordinates: Visual Multidimensional Geometry and Its Applications. Springer, 2009.
[14] A. Inselberg and B. Dimsdale, Parallel coordinates: A tool for visualizing multi-dimensional geometry. In IEEE Visualization, pages 361–378. IEEE CS Press, 1990.
[15] J. Johansson and M. Cooper, A screen space quality method for data abstraction. Comput. Graph. Forum, 27 (3): 1039–1046, 2008.
[16] D. Keim, Designing pixel-oriented visualization techniques: theory and applications. IEEE Transactions on Visualization and Computer Graphics, 6: 59–78, 2000.
[17] J. Li, J.-B. Martens, and J. J. van Wijk, Judging correlation from scatter-plots and parallel coordinate plots. Information Visualization, 9 (1): 13–30, 2010.
[18] M. Lind, J. Johansson, and M. Cooper, Many-to-Many Relational Parallel Coordinates Displays. 2009 13th International Conference Information Visualisation, pages 25–31, July 2009.
[19] N. Miller, B. Hetzler, G. Nakamura, and P. Whitney, The need for metrics in visual information analysis. In NPIV '97: Proceedings of the 1997 workshop on New paradigms in information visualization and manipulation, pages 24–28, New York, NY, USA, 1997. ACM.
[20] J.-F. Rit, Propagating temporal constraints for scheduling. In Proceedings of the Fifth National Conference on Artificial Intelligence, pages 383–388, 1986.
[21] J. Schneidewind, M. Sips, and D. A. Keim, Pixnostics: Towards measuring the value of visualization. In Proceedings Visual Analytics Science and Technology, pages 199–206. IEEE CS Press, 2006.
[22] B. Shneiderman, The eyes have it: A task by data type taxonomy for information visualizations. In Proceedings Visual Languages, pages 336–343. IEEE CS Press, 1996.
[23] A. Tatu, G. Albuquerque, M. Eisemann, H. Theisel, M. Magnor, and D. Keim, Combining automated analysis and visualization techniques for effective exploration of high-dimensional data. IEEE Symposium on Visual Analytics Science and Technology, pages 59–66, 2009.
[24] E. R. Tufte, The Visual Display of Quantitative Information. Graphics Press, 2nd edition, 2001.
[25] J. Tukey and P. Tukey, Computing graphics and exploratory data analysis: An introduction. In Proceedings of the Sixth Annual Conference and Exposition: Computer Graphics 85. In Proceedings of the Sixth Annual Conference and Exposition: Computer Graphics, pages 773–785, 1985.
[26] UC Irvine Machine Learning Repository. http://archive.ics.uci.eduml/.
[27] P. Vázquez, M. Feixas, M. Sbert, and W. Heidrich, Viewpoint selection using viewpoint entropy. In Proceedings Vision, Modeling, and Visualization, pages 273–280, 2001.
[28] C. Ware, H. Purchase, L. Colpoys, and M. McGill, Cognitive measurements of graph aesthetics. Information Visualization, 1 (2): 103–110, 2002.
[29] E. Wegman, Hyperdimensional data analysis using parallel coordinates. Journal of the American Statistical Association, 85, 1990.
[30] L. Wilkinson, A. Anand, and R. Grossman, Graph-theoretic scagnos-tics. In Proceedings Information Visualization, pages 157–164. IEEE CS Press, 2005.
[31] J. Yang, M. Ward, E. Rundensteiner, and S. Huang, Visual hierarchical dimension reduction for exploration of high dimensional datasets. In Proceedings Data Visualization, pages 19–28. Eurographics Press, 2003.
[32] H. Zhou, X. Yuan, H. Qu, W. Cui, and B. Chen, Visual clustering in parallel coordinates. Computer Graphics Forum, 27 (3): 1047–1054, 2008.

Index Terms:
Parallel Coordinates, metrics, display optimization, visualization models.
Citation:
Aritra Dasgupta, Robert Kosara, "Pargnostics: Screen-Space Metrics for Parallel Coordinates," IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 6, pp. 1017-1026, Nov.-Dec. 2010, doi:10.1109/TVCG.2010.184
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