Graph-Based Visual Analytic Tools for Parallel Coordinates

  • Kai Lun Chung
  • Wei Zhuo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5359)


Parallel coordinates is a fundamental visualization technique in multivariate data visualization. Visual clutter is one of the inherent weaknesses in parallel coordinates. In this paper, we present two visual analytic tools, the Selection Graph and the Relation Graph, to reduce the visual clutter. The Selection Graph is a brushing tool which helps users highlight the regions of interest. The Relation Graph organizes clusters in a structural manner, providing an intuitive interface for users to explore relations among clusters. Both tools neither distort nor filter the underlying data in parallel coordinates. The experiments on several real datasets demonstrate the effectiveness of our tools.


Encode Scheme Local Cluster Relation Graph Visual Cluster Parallel Coordinate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Inselberg, A.: The plane with parallel coordinates. Journal The Visual Computer 1, 69–91 (1985)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Inselberg, A., Dimsdale, B.: Parallel coordinates: a tool for visualizing multi-dimensional geometry. In: VIS 1990: Proceedings of the 1st conference on Visualization 1990, pp. 361–378. IEEE Computer Society Press, Los Alamitos (1990)Google Scholar
  3. 3.
    Zhou, H., Yuan, X., Qu, H., Cui, W., Chen, B.: Visual clustering in parallel coordinates. Computer Graphics Forum 27 (2008)Google Scholar
  4. 4.
    McDonnell, K., Mueller, K.: Illustrative parallel coordinates. Computer Graphics Forum (Special Issue Eurovis 2008) 27, 1027–1031 (2008)Google Scholar
  5. 5.
    Ward, M.O.: Xmdvtool: integrating multiple methods for visualizing multivariate data. In: VIS 1994: Proceedings of the conference on Visualization 1994, pp. 326–333. IEEE Computer Society Press, Los Alamitos (1994)Google Scholar
  6. 6.
    Wong, P.C., Bergeron, R.D.: Multiresolution multidimensional wavelet brushing. In: VIS 1996: Proceedings of the 7th conference on Visualization 1996, p. 141. IEEE Computer Society Press, Los Alamitos (1996)Google Scholar
  7. 7.
    Fua, Y.H., Ward, M.O., Rundensteiner, E.A.: Structure-based brushes: A mechanism for navigating hierarchically organized data and information spaces. IEEE Transactions on Visualization and Computer Graphics 6, 150–159 (2000)CrossRefGoogle Scholar
  8. 8.
    Hauser, H., Ledermann, F., Doleisch, H.: Angular brushing of extended parallel coordinates. In: INFOVIS 2002: Proceedings of the IEEE Symposium on Information Visualization (InfoVis 2002), Washington, DC, USA, p. 127. IEEE Computer Society, Los Alamitos (2002)CrossRefGoogle Scholar
  9. 9.
    Ericson, D., Johansson, J., Cooper, M.: Visual data analysis using tracked statistical measures within parallel coordinate representations. In: CMV 2005: Proceedings of the Coordinated and Multiple Views in Exploratory Visualization, Washington, DC, USA, pp. 42–53. IEEE Computer Society, Los Alamitos (2005)Google Scholar
  10. 10.
    Qu, H., Chan, W.Y., Xu, A., Chung, K.L., Guo, P., Lau, K.H.: Visual analysis of the air pollution problem in hong kong. IEEE Transactions on Visualization and Computer Graphics 13, 1408–1415 (2007)CrossRefGoogle Scholar
  11. 11.
    Hartigan, J.A., Wong, M.A.: A K-means clustering algorithm. Applied Statistics 28, 100–108 (1979)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Kai Lun Chung
    • 1
  • Wei Zhuo
    • 1
  1. 1.The Hong Kong University of Science and TechnologyHong Kong

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