Discovering Web Usage Patterns - A Novel Approach

  • K. Sudheer Reddy
  • Ch. N. Santhosh Kumar
  • V. Sitaramulu
  • M. Kantha Reddy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 199)


Pattern mining is one of the most pivotal steps in data mining; pattern mining immediately comes after the preprocessing phase of WUM. Pattern discovery deals with the sorted set of data items presented as part of the sequence. Pattern mining, users can recognize the web paths follow on a web site easily. The aim of this research discovers the patterns which are most relevant and interesting by using a Web usage mining process. The server web logs aids are the input to this process. Our target is to discover users’ behavior, who has visited the web sites for less number of times. We have enlightened a method for clustering, based on the pattern summaries. We have conducted intense experiments and the results are shown in this paper.


Web usage mining preprocessing pattern discovery sequential patterns clustering patterns summary 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Benedek, A., Trousse, B.: Adaptation of Self-Organizing Maps for CBR case indexing. In: 27th Annual Conference of the Gesellschaft fur Klassifikation, Cottbus, Germany (March 2003)Google Scholar
  2. 2.
    Fayad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.): Advances in Knowledge Discovery and Data Mining. AAAI Press, Menlo Park (1996)Google Scholar
  3. 3.
    Giacometti, A.: Modèles hybrides de l’expertise, novembre, PhD Thesis, ENST Paris (1992) (in French)Google Scholar
  4. 4.
    Cooley, R., Mobasher, B., Srivastava, J.: Data preparation for mining world wide web browsing patterns. Knowledge and Information Systems 1(1), 5–32 (1999)CrossRefGoogle Scholar
  5. 5.
    Jaczynski, M.: Modèle et plate-forme à objets pour l’indexation des cas par situation comportementales: application à l’assistance à la navigation sur le web, décembre, PhD thesis, Université de Nice Sophia-Antipolis (1998) (in French)Google Scholar
  6. 6.
    Malek, M.: Un modèle hybride de mémoire pour le raisonnementà partir de cas, PhD thesis, Universitẽ Joseph Fourrier (Octobre 1996) (in French)Google Scholar
  7. 7.
    Masseglia, F., Poncelet, P., Cicchetti, R.: An efficient algorithm for web usage mining. Networking and Information Systems Journal (NIS) (April 2000)Google Scholar
  8. 8.
    Srikant, R., Agrawal, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 3–17. Springer, Heidelberg (1996)Google Scholar
  9. 9.
    Tanasa, D., Trousse, B.: Web access pattern discovery and analysis based on page classification and on indexing sessions with a generalised suffix tree. In: Proceedings of the 3rd International Workshop on Symbolic and Numeric Algorithms for Scientific Computing, Timisoara, Romania, pp. 62–72 (October 2001)Google Scholar
  10. 10.
  11. 11.
    Masseglia, F., Cathala, F., Poncelet, P.: The PSP Approach for Mining Sequential Patterns. In: Żytkow, J.M. (ed.) PKDD 1998. LNCS, vol. 1510, pp. 176–184. Springer, Heidelberg (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • K. Sudheer Reddy
    • 1
  • Ch. N. Santhosh Kumar
    • 2
  • V. Sitaramulu
    • 2
  • M. Kantha Reddy
    • 3
  1. 1.Dept. of CSEAcharya Nagarjuna UniversityGunturIndia
  2. 2.Department of Computer Science & EngineeringSwarna Bharathi Institute of Science & TechnologyKhammamIndia
  3. 3.IUCEEVadlamudiIndia

Personalised recommendations