Towards Recommender System Using Particle Swarm Optimization Based Web Usage Clustering

  • Shafiq Alam
  • Gillian Dobbie
  • Patricia Riddle
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7104)


Efficiency and quality of the product of data mining process is a challenging question for the researchers. Different methods have been proposed in the literature to tackle these problems. Optimization based methods are a way to address this issue. We addressed the problem of data clustering by implementing swarm intelligence based optimization technique called Particle Swarm Optimization (PSO). We scaled the approach to implement it in a hierarchical way using Hierarchical Particle Swarm (HPSO) clustering. The paper also aims to outline our novel outlier detection technique. The research will lead us to provide a benchmark for web usage mining and propose a collective intelligence based recommender system for the usage of Java API documentation.


Swarm intelligence clustering recommender system outlier detection 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Alam, S., Dobbie, G., Riddle, P.: An evolutionary particle swarm optimization algorithm for data clustering. In: Swarm Intelligence Symposium, SIS 2008, pp. 1–6. IEEE (2008)Google Scholar
  2. 2.
    Alam, S., Dobbie, G., Riddle, P.: Particle swarm optimization based clustering of web usage data. In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2008, vol. 3, pp. 451–454 (2008)Google Scholar
  3. 3.
    Banerjee, A., Ghosh, J.: Clickstream clustering using weighted longest common subsequences. In: Proceedings of the Web Mining Workshop at the 1st SIAM Conference on Data Mining, pp. 33–40 (2001)Google Scholar
  4. 4.
    Cao, L.: In-depth behavior understanding and use: the behavior informatics approach. Information Sciences, 3067–3085 (2010)Google Scholar
  5. 5.
    Chen, J., Zhang, H.: Research on application of clustering algorithm based on pso for the web usage pattern. In: International Conference on Wireless Communications, Networking and Mobile Computing, WiCom 2007, pp. 3705–3708 (2007)Google Scholar
  6. 6.
    Cohen, S.C.M., De Castro, L.N.: Data clustering with particle swarms. In: 2006 IEEE International Conference on Evolutionary Computation, pp. 1792–1798 (2006)Google Scholar
  7. 7.
    Eberhart, R.C., Shi, Y., Kennedy, J.: Swarm Intelligence, 1st edn. Morgan Kaufmann (2001)Google Scholar
  8. 8.
    Edelstein, H.: Introduction to data mining and knowledge discovery, 3rd edn. Two Crows Corp. (1999)Google Scholar
  9. 9.
    Engelbrecht, A.P.: Fundamentals of Computational Swarm Intelligence. John Wiley & Sons (2006)Google Scholar
  10. 10.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. of 2nd International Conference on Knowledge Discovery, pp. 226–231 (1996)Google Scholar
  11. 11.
    Frawley, W.J., Piatetsky-Shapiro, G., Matheus, C.J.: Knowledge discovery in databases: An overview. In: Knowledge Discovery in Databases, pp. 1–30. AAAI/MIT Press (1991)Google Scholar
  12. 12.
    Fu, Y., Sandhu, K., Shih, M.-Y.: A Generalization-Based Approach to Clustering of Web Usage Sessions. In: Masand, B., Spiliopoulou, M. (eds.) WebKDD 1999. LNCS (LNAI), vol. 1836, pp. 21–38. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  13. 13.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. of International Conference on Neural Networks IV, pp. 1942–1948 (1995)Google Scholar
  14. 14.
    Kuo, R., Wang, M., Huang, T.: An application of particle swarm optimization algorithm to clustering analysis. Soft Computing - A Fusion of Foundations, Methodologies and Applications 15, 533–542 (2011)Google Scholar
  15. 15.
    van der Merwe, D., Engelbrecht, A.: Data clustering using particle swarm optimization. In: The 2003 Congress on Evolutionary Computation, CEC 2003, vol. 1, pp. 215–220 (2003)Google Scholar
  16. 16.
    Omran, M., Salman, A., Engelbrecht, A.: Dynamic clustering using particle swarm optimization with application in image segmentation. Pattern Analysis and Applications 8, 332–344 (2006)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Shahabi, C., Zarkesh, A., Adibi, J., Shah, V.: Knowledge discovery from users web-page navigation. In: Proceedings of Seventh International Workshop on Research Issues in Data Engineering, pp. 20–29 (April 1997)Google Scholar
  18. 18.
    Xiao, X., Dow, E.R., Eberhart, R., Miled, Z.B., Oppelt, R.J.: Gene Clustering using Self-Organizing Maps and Particle Swarm Optimization. In: Guo, M. (ed.) ISPA 2003. LNCS, vol. 2745, pp. 154–160. Springer, Heidelberg (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Shafiq Alam
    • 1
  • Gillian Dobbie
    • 1
  • Patricia Riddle
    • 1
  1. 1.Department of Computer ScienceUniversity of AucklandNew Zealand

Personalised recommendations