Clickstream Visualization Based on Usage Patterns

  • Srinidhi Kannappady
  • Sudhir P. Mudur
  • Nematollaah Shiri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4338)


Most clickstream visualization techniques display web users’ clicks by highlighting paths in a graph of the underlying web site structure. These techniques do not scale to handle high volume web usage data. Further, historical usage data is not considered. The work described in this paper differs from other work in the following aspect. Fuzzy clustering is applied to historical usage data and the result imaged in the form of a point cloud. Web navigation data from active users are shown as animated paths in this point cloud. It is clear that when many paths get attracted to one of the clusters, that particular cluster is currently “hot.” Further as sessions terminate, new sessions are incrementally incorporated into the point cloud. The complete process is closely coupled to the fuzzy clustering technique and makes effective use of clustering results. The method is demonstrated on a very large set of web log records consisting of over half a million page clicks.


Point Cloud Cluster Center Fuzzy Cluster Multi Dimensional Scaling Information Visualization 
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.
    Andrews, K.: Visualizing Cyberspace: information visualization in the harmony internet browser. In: Proc. 1st IEEE Symp. On Information Visualization, pp. 90–96 (1995)Google Scholar
  2. 2.
    Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases (1998)Google Scholar
  3. 3.
    Brainerd, J., Becker, B.: Case Study: E-commerce Clickstream Visualization. In: Proc. of the IEEE Symp. On Information Visualization, pp. 153–156 (2001)Google Scholar
  4. 4.
    Chi, E.H.: Improving Web Usability through Visualization. Internet Computing 6(2), 64–71 (2002)CrossRefGoogle Scholar
  5. 5.
    Chi, E.H.: WebSpace Visualizations. In: Proc. 2nd Int’l World Wide Consortium (W3C), IEEE Internet Computing, vol. 6(2), pp. 64–71 (1994)Google Scholar
  6. 6.
    Cugini, J., Scholtz, J.: VISIP: 3D Visualization of Paths through Websites. In: Proc. Int’l workshop on Web-Based Information Visualization, Florence, Italy, pp. 259–263 (1999)Google Scholar
  7. 7.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2000)Google Scholar
  8. 8.
    Herman, I., Melancon, G., Marshall, M.S.: Graph Visualization and Navigation in Information Visualization: a survey. IEEE TVCG 6(1), 24–43 (2000)Google Scholar
  9. 9.
    Hong, J.I., Landay, J.A.: WebQuilt: A Framework for Capturing and Visualizing the Web Experience. In: Proc. 10th Int’l World Wide Web Conference, Hong Kong, China, pp. 717–724 (2001)Google Scholar
  10. 10.
    Inselberg, A., Dimsdale, B.: Parallel coordinates: A tool for visualizing multi-dimensional geometry. In: Proc. Visualization 1990, San Francisco, CA, USA, pp. 361–370 (1999)Google Scholar
  11. 11.
    Kannappady, S., Mudur, S.P., Shiri, N.: Visualization of Web Usage Patterns. In: Proc. 10th Int’l Database Engineering & Applications Symposium (IDEAS), New Delhi, India (2006)Google Scholar
  12. 12.
    Kruskal, J.B.: Multidimensional Scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29(1), 1–27 (1964)MATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Lee, J., Podlaseck, M., Schonberg, E., Hoch, R.: Visualization and Analysis of Clickstream Data of Online Stores for Understanding Web Merchandising. Int’l Journal of Data Mining and Knowledge Discovery 5(1) (2001)Google Scholar
  14. 14.
    Lopez, N., Kreuseler, S.H.: A scalable framework for information visualization. Trans. on Visualization and Computer Graphics (2001)Google Scholar
  15. 15.
    Munzner, T.: Drawing Large Graphs with H3Viewer and Site Manager. In: Whitesides, S.H. (ed.) GD 1998. LNCS, vol. 1547, pp. 384–393. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  16. 16.
    Nasraoui, O., Krishnapuram, R., Joshi, A., Kamdar, T.: Automatic Web User Profiling and Personalization using Robust Fuzzy Relational Clustering. In: E-commerce and Intelligent Methods, Springer, Heidelberg (2002)Google Scholar
  17. 17.
    Sammon Jr., J.W.: A non-linear mapping for data structure analysis. IEEE Trans. on Computers 18, 401–409 (1969)CrossRefGoogle Scholar
  18. 18.
    Simonson, J., Fuller, G., Tiwari, A.: A Survey of Web History Data Analysis and Visualization. In:
  19. 19.
    Suryavanshi, B.S., Shiri, N., Mudur, S.P.: An Efficient Technique for Mining Usage Profiles Using Relation Fuzzy Subtractive Clustering. In: Proc. Int’l workshop on Challenges in Web Information retrieval and Integration, pp. 23–29 (2005)Google Scholar
  20. 20.
    Suryavanshi, B.S., Shiri, N., Mudur, S.P.: Incremental Relational Fuzzy Subtractive Clustering for Dynamic Web Usage Profiling. In: Proc. WEBKDD Workshop on Training Evolving, Expanding and Multi-faceted Web Clickstreams, Chicago, Illinois, USA (2005)Google Scholar
  21. 21.
    Trevor, F.C., Michael, A.A.C.: Multidimensional Scaling, 2nd edn. Chapman and Hall (2001)Google Scholar
  22. 22.
  23. 23.
    Wills, G.J.: Nicheworks-Interactive Visualization of Very Large Graphs. In: DiBattista, G. (ed.) GD 1997. LNCS, vol. 1353, Springer, Heidelberg (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Srinidhi Kannappady
    • 1
  • Sudhir P. Mudur
    • 1
  • Nematollaah Shiri
    • 1
  1. 1.Dept. of Computer Science and Software EngineeringConcordia UniversityMontreal, QuebecCanada

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