iSPLOM: Interactive with Scatterplot Matrix for Exploring Multidimensional Data

  • Tran Van LongEmail author
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 244)


The scatterplot matrix is one of the most common methods for multidimensional data visualization. The scatterplot matrix is usually used to display all pairwise of data dimensions, that is organized a matrix. In this paper, we propose an interactive technique with scatterplot matrix to explore multidimensional data. The multidimensional data is projected into all pairwise orthogonal sections and display with scatterplot matrix. A user can select a subset of data set that separates from the rest of data set. A subset of the data set organized as a hierarchy cluster structure. The hierarchy cluster structure is display as a radial tree cluster. The user can select a cluster in the radial tree and all data points in this cluster are display on scatterplot matrix. The user is repeated this process to identify clusters. One of the most useful of our method can be identify the structure of multidimensional data set in an intuition fashion.


Visualization Technique Cluster Tree Multidimensional Data Information Visualization Hierarchy Cluster 
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.
    Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic subspace clustering of high dimensional data for data mining applications. In: Proceeding SIGMOD 1998 Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, pp. 94–105 (1998)Google Scholar
  2. 2.
    Albuquerque, G., Eisemann, M., Lehmann, D.J., Theisel, H., Magnor, M.: Quality-based visualization matrices. In: Proc. Vision, Modeling and Visualization (VMV), Braunschweig, Germany, pp. 341–349 (November 2009)Google Scholar
  3. 3.
    Becker, R.A., Cleveland, W.S.: Brushing scatterplots. Technometrics 29(2), 127–142 (1987)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Carr, D.B., Littlefield, R.J., Nicholson, W.L., Littlefield, J.S.: Scatterplot matrix techniques for large N. Journal of the American Statistical Association 82(398), 424–436 (1987)MathSciNetGoogle Scholar
  5. 5.
    Cui, Q., Ward, M.O., Rundensteiner, E.A.: Enhancing scatterplot matrices for data with ordering or spatial attributes. In: SPIE Proceedings. Visualization and Data Analysis, vol. 6060 (2006)Google Scholar
  6. 6.
    Chambers, J.M., Cleveland, W.S., Tukey, P.A., Kleiner, B.: Graphical Methods for Data Analysis. Wadsworth Press, Belmont (1983)zbMATHGoogle Scholar
  7. 7.
    Chen, K., Liu, L.: VISTA: Validating and refining clusters via visualization. Information Visualization 3(4), 257–270 (2004)CrossRefGoogle Scholar
  8. 8.
    Chen, K., Liu, L.: Clustermap: Labeling clusters in large datasets via visualization. In: Proceedings of the Thirteenth ACM International Conference on Information and Knowledge Management, pp. 285–293 (2004)Google Scholar
  9. 9.
    Chen, K., Liu, L.: iVIBRATE: Interactive visualization-based framework for clustering large datasets. ACM Transactions on Information Systems (TOIS) 24(2), 245–294 (2006)CrossRefzbMATHGoogle Scholar
  10. 10.
    Elmqvist, N., Dragicevic, P., Fekete, J.-D.: Rolling the dice: Multidimensional visual exploration using scatterplot matrix navigation. IEEE Transactions on Visualization and Computer Graphics 14(6), 1539–1148 (2008)Google Scholar
  11. 11.
    Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. Journal Data Mining and Knowledge Discovery archive 2(2), 169–194 (1998)CrossRefGoogle Scholar
  12. 12.
    Fua, Y.-H., Ward, M.O., Rundensteiner, E.A.: Hierarchical parallel coordinates for exploration of large datasets. In: Proceedings of the Conference on Visualization 1999: Celebrating Ten Years, pp. 43–50. IEEE Computer Society Press (1999)Google Scholar
  13. 13.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. The Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann Publisher (2012)Google Scholar
  14. 14.
    Hinneburg, A., Keim, D.A.: An efficient approach to clustering in large multimedia databases with noise. In: Proceedings of the 4th International Conference on Knowledge Discovery and Datamining (KDD 1998), New York, NY, pp. 58–65 (September 1998)Google Scholar
  15. 15.
    Hinneburg, A., Keim, D.A., Wawryniuk, M.: HD-Eye: Visual mining of high-dimensional data. IEEE Computer Graphics and Applications 19(5), 22–31 (1999)CrossRefGoogle Scholar
  16. 16.
    Inselberg, A.: Parallel Coordinates: Visual Multidimensional Geometry and its Applications. Springer (2009)Google Scholar
  17. 17.
    Kandogan, E.: Star coordinates: A multi-dimensional visualization technique with uniform treatment of dimensions. In: Proceedings of the IEEE Information Visualization Symposium, vol. 650 (2000)Google Scholar
  18. 18.
    Van Long, T., Linsen, L.: MultiClusterTree: interactive visual exploration of hierarchical clusters in multidimensional multivariate data. Computer Graphics Forum 28(3), 823–830 (2009)CrossRefGoogle Scholar
  19. 19.
    Seo, J., Shneiderman, B.: Interactively exploring hierarchical clustering results. Computer 35(7), 80–86 (2002)CrossRefGoogle Scholar
  20. 20.
    Sips, M., Neubert, B., Lewis, J.P., Hanrahan, P.: Selecting good views of high-dimensional data using class consistency. Computer Graphics Forum 28(3), 831–838 (2009)CrossRefGoogle Scholar
  21. 21.
    Tatu, A., Albuquerque, G., Eisemann, M., Schneidewind, J., Theisel, H., Magnork, M., Keim, D.: Combining automated analysis and visualization techniques for effective exploration of high-dimensional data. In: IEEE Symposium on Visual Analytics Science and Technology, VAST 2009, pp. 59–66 (2009)Google Scholar
  22. 22.
    Viau, C., McGuffin, M.J., Chiricota, Y., Igor, J.: The flowvizmenu and parallel scatterplot matrix: Hybrid multidimensional visualizations for network exploration. IEEE Transactions on Visualization and Computer Graphics 16(6), 1100–1108 (2010)CrossRefGoogle Scholar
  23. 23.
    Ward, M.O.: XmdvTool: Integrating Multiple Methods for Visualizing Multivariate Data. In: IEEE Conf. on Visualization 1994, pp. 326–333 (October 1994),
  24. 24.
    Wei, W., Yang, J., Muntz, R.: STING: A statistical information grid approach to spatial data mining. In: Proceedings of the International Conference on Very Large Data Bases, pp. 186–195. Institute of Electrical and Electronics Engineers, IEEE (1997)Google Scholar
  25. 25.
    Wilkinson, L., Anand, A., Grossman, R.: Graph-theoretic scagnostics. In: IEEE Symposium on Information Visualization, INFOVIS 2005, pp. 157–164 (2005)Google Scholar
  26. 26.
    Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: an efficient data clustering method for very large databases. ACM SIGMOD Record 25(2), 103–114 (1996)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Basic SciencesUniversity of Transport and CommunicationsHanoiVietnam

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