This Article 
 Bibliographic References 
 Add to: 
Flexible Linked Axes for Multivariate Data Visualization
Dec. 2011 (vol. 17 no. 12)
pp. 2310-2316
Jarry H.T. Claessen, Eindhoven University of Technology
Jarke J. van Wijk, Eindhoven University of Technology
Multivariate data visualization is a classic topic, for which many solutions have been proposed, each with its own strengths and weaknesses. In standard solutions the structure of the visualization is fixed, we explore how to give the user more freedom to define visualizations. Our new approach is based on the usage of Flexible Linked Axes: The user is enabled to define a visualization by drawing and linking axes on a canvas. Each axis has an associated attribute and range, which can be adapted. Links between pairs of axes are used to show data in either scatter plot- or Parallel Coordinates Plot-style. Flexible Linked Axes enable users to define a wide variety of different visualizations. These include standard methods, such as scatter plot matrices, radar charts, and PCPs [11]; less well known approaches, such as Hyperboxes [1], TimeWheels [17], and many-to-many relational parallel coordinate displays [14]; and also custom visualizations, consisting of combinations of scatter plots and PCPs. Furthermore, our method allows users to define composite visualizations that automatically support brushing and linking. We have discussed our approach with ten prospective users, who found the concept easy to understand and highly promising.

[1] B. Alpern and L. Carter, The hyperbox. In Proceedings 2nd IEEE Conference on Visualization (Vis'91), pages 133–139, 1991.
[2] R. A. Amar, J. Eagan, and J. T. Stasko, Low-level components of analytic activity in information visualization. In Proceedings IEEE Symposium on Information Visualization (InfoVis 2005), pages 111–117, 2005.
[3] R. Becker and W. Cleveland, Brushing scatterplots. Techmometric, 29 (2): 127–142, 1987.
[4] C. Collins and M. S. T. Carpendale, Vislink: Revealing relationships amongst visualizations. IEEE Transactions on Visualization and Computer Graphics, 13 (6): 1192–1199, 2007.
[5] 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.
[6] N. Elmqvist, P. Dragicevic, and J.-D. Fekete, Rolling the dice: Multidimensional visual exploration using scatterplot matrix navigation. IEEE Transactions on Visualization and Computer Graphics, 14 (6): 1141– 1148, 2008.
[7] M. Graham and J. Kennedy, Using curves to enhance parallel coordinate visualizations. In Proceedings Information Visualisation (IV 2003), pages 10–16, 2003.
[8] G. Grinstein, M. Trutschl, and U. Cvek, High-dimensional visualizations”. In Proceedings International Workshop on Visual Data Mining 2001, pages 7–19, 2001.
[9] H. Hauser, F. Ledermann, and H. Doleisch, Angular brushing of extended parallel coordinates. In Proceedings IEEE Symposium on Information Visualization (InfoVis 2002), pages 127–130, 2002.
[10] D. Holten and J. J. van Wijk, Evaluation of cluster identification performance for different PCP variants. Computer Graphics Forum, 29 (3): 793– 802, 2010.
[11] A. Inselberg, The plane with parallel coordinates. The Visual Computer, 1 (4): 69–97, 1985.
[12] D. Keim, Information visualization and visual data mining. IEEE Transactions on Visualization and Computer Graphics, 8 (1): 1–8, 2002.
[13] D. Keim and H.-P. Kriegel, Visualization techniques for mining large databases: a comparison. IEEE Transactions on Knowledge and Data Engineering, 8 (6): 923–936, 1996.
[14] M. Lind, J. Johansson, and M. Cooper, Many-to-many relational parallel coordinates displays. In Proceedings Information Visualisation (IV 2009), pages 25–31, 2009.
[15] M. Novotny and H. Hauser, Outlier-preserving focus+context visualization in parallel coordinates. IEEE Transactions on Visualization and Computer Graphics, 12 (5): 893–900, 2006.
[16] E. Ramos and D. Donoho, The 1983 ASA data exposition dataset: Cars. , 1983.
[17] C. Tominski, J. Abello, and H. Schumann, Axes-based visualizations with radial layouts. In Proceedings of the 2004 ACM symposium on Applied Computing (SAC'04), pages 1242–1247, 2004.
[18] C. Viau, M. McGuffin, Y. Chiricota, and I. Jurisica, The flowvizmenu and parallel scatterplot matrix: Hybrid multidimensional visualizations for network exploration. IEEE Transactions on Visualization and Computer Graphics, 16 (6): 1100–1108, 2010.
[19] L. Wilkinson, A. Anand, and R. Grossman, Graph-theoretic scagnostics. In Proceedings IEEE Symposium on Information Visualization (InfoVis 2005), pages 157–164, 2005.
[20] P. C. Wong and R. D. Bergeron, 30 years of multidimensional multivariate visualization. In Scientific Visualization Overviews, Methodologies, and Techniques, pages 3–33. IEEE Computer Society Press, 1997.
[21] 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:
Multivariate data, visualization, scatter plot, Parallel Coordinates Plot.
Jarry H.T. Claessen, Jarke J. van Wijk, "Flexible Linked Axes for Multivariate Data Visualization," IEEE Transactions on Visualization and Computer Graphics, vol. 17, no. 12, pp. 2310-2316, Dec. 2011, doi:10.1109/TVCG.2011.201
Usage of this product signifies your acceptance of the Terms of Use.