Extracting Sequential Patterns Based on User Defined Criteria

  • Oznur Kirmemis Alkan
  • Pınar Karagoz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8073)


Sequential pattern extraction is essential in many applications like bioinformatics and consumer behavior analysis. Various frequent sequential pattern mining algorithms have been developed that mine the set of frequent subsequences satisfying a minimum support constraint in a transaction database. In this paper, a hybrid framework to sequential pattern mining problem is proposed which combines clustering together with a novel pattern extraction algorithm that is based on an evaluation function, which utilizes user-defined criteria to select patterns. The proposed solution is applied on Web log data and Web domain, however, it can work in any other domain that involves sequential data as well. Through experimental evaluation on two different datasets, we show that the proposed framework can achieve valuable results for extracting patterns under user defined selection criteria.


equential Pattern User-defined selection criteria Clustering PatternFindBF Web Usage Pattern 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Leung, C.W., Chan, S.C., Chung, F.: A Collaborative Filtering Framework Based on Fuzzy Association Rules and Multiple-Level Similarity. Knowl. Inf. Syst. 10(3), 357–381 (2006)CrossRefGoogle Scholar
  2. 2.
    Senkul, P., Salin, S.: Improving Pattern Quality in Web Usage Mining by Using Semantic Information. Knowl. Inf. Syst. 30(3), 527–541 (2011)CrossRefGoogle Scholar
  3. 3.
    Shyu, M., Haruechaiyasak, C., Chen, S.: Mining User Access Patterns with Traversal Constraint for Predicting Web Page Requests. Knowl. Inf. Syst. 10(4), 515–528 (2006)CrossRefGoogle Scholar
  4. 4.
    Bonnin, G., Brun, A., Boyer, A.: A Low-Order Markov Model Integrating Long-Distance Histories for Collaborative Recommender Systems. In: Proceedings of the 14th International Conference on Intelligent User Interfaces, Florida, USA, pp. 57–66 (2009)Google Scholar
  5. 5.
    Mooney, C.H., Roddick, J.F.: Sequential Pattern Mining – Approaches and Algorithms. ACM Computing Surveys (CSUR) Surveys Homepage Archive 45(2) (2013)Google Scholar
  6. 6.
    Kirmemis Alkan, O., Karagoz, P.: Assisting Web Site Navigation Through Web Usage Patterns. In: IEA/AIE 2013, Amsterndam, Netherlands (June 2013)Google Scholar
  7. 7.
    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. 1–17. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  8. 8.
    Ren, J.D., Cheng, Y.B., Yang, L.L.: An Algorithm for Mining Generalized Sequential Patterns. In: Proceedings of International Conference on Machine Learning and Cybernetics, vol. 2, pp. 1288–1292 (2004)Google Scholar
  9. 9.
    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 (2001)Google Scholar
  10. 10.
    Daoud, M., Lechani, L., Boughanem, M.: Towards a Graph-Based User Profile Modeling for a Session Based Personalized Search. Knowl. Inf. Syst. 21(3), 365–398 (2009)CrossRefGoogle Scholar
  11. 11.
    Kothari, R., Mittal, P.A., Jain, V., Mohania, M.K.: On Using Page Cooccurrences for Computing Clickstream Similarity. In: Proceedings of the 3rd SIAM International Conference on Data Mining, San Francisco, USA (2003)Google Scholar
  12. 12.
    Eiron, N., McCurley, K.S.: Untangling Compound Documents on the Web. In: Proceedings of ACM Hypertext, pp. 85–94 (2003)Google Scholar
  13. 13.
    Flake, G.W., Lawrence, S., Giles, C.L., Coetzee, F.: Self-Organization and Identification of Web Communities. IEEE Computer 35(3) (2002)Google Scholar
  14. 14.
    Greco, G., Greco, S., Zumpano, E.: Web Communities: Models and Algorithms. World Wide Web 7(1), 58–82 (2004)CrossRefGoogle Scholar
  15. 15.
    Xie, Y., Phoha, V.V.: Web User Clustering from Access Log Using Belief Function. In: Proceedings of the First International Conference on Knowledge Capture, pp. 202–208. ACM Press (2001)Google Scholar
  16. 16.
    Pinto, H., Han, J., Pei, J., Wang, K.: Multi-dimensional Sequence Pattern Mining. In: CIKM (2001)Google Scholar
  17. 17.
    Bezerra, B.L.D., Carvalho, F.A.T.: Symbolic Data Analysis Tools for Recommendation Systems. Knowl. Inf. Syst. 21(3), 385–418 (2010)Google Scholar
  18. 18.
    Nasraoui, O., Gonzalez, F., Dasgupta, D.: The Fuzzy Artificial Immune System: Motivations, Basic Concepts, and Application to Clustering and Web Profiling. In: Proceedings of the World Congress on Computational Intelligence (WCCI) and IEEE International Conference on Fuzzy Systems, pp. 711–716 (2002)Google Scholar
  19. 19.
    Mobasher, B., Dai, H., Tao, M.: Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization. Data Mining and Knowledge Discovery 6, 61–82 (2002)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Gunduz, S., Ozsu, M.T.: A Web Page Prediction Model Based on Clickstream Tree Representation of User Behavior. In: SIGKDD 2003, USA, pp. 535–540 (2003)Google Scholar
  21. 21.
    Patil, S.S.: A Least Square Approach to Analyze Usage Data for Effective Web Personalization. In: Proceedings of International Conference on Advances in Computer Science, pp. 110–114 (2011)Google Scholar
  22. 22.
    Cooley, R., Mobasher, B., Srivastava, J.: Web Mining: Information and Pattern Discovery on the World Wide Web. In: International Conference on Tools with Artificial Intelligence, Newport Beach, pp. 558–567. IEEE (1997)Google Scholar
  23. 23.
    Yang, Q., Fan, J., Wang, J., Zhou, L.: Personalizing Web Page Recommendation via Collaborative Filtering and Topic-Aware Markov Model. In: Proceedings of the 10th International Conference on Data Mining, ICDM, Sydney, pp. 1145–1150 (2010)Google Scholar
  24. 24.
    Pei, J., Han, J., Mortazavi-Asi, B., Pino, H.: PrefixSpan: Mining Sequential Patterns Efficiently by Prefix- Projected Pattern Growth. In: ICDE 2001 (2001)Google Scholar
  25. 25.
    Mobasher, B.: Data Mining for Web Personalization. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 90–135. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  26. 26.
    Srivastava, J., Cooley, R., Deshpande, M., Tan, P.: Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. SIGDD Explorations 1(2), 12–23 (2000)CrossRefGoogle Scholar
  27. 27.
    Cooley, R., Mobasher, B., Srivastava, J.: Data Preparation for Mining World Wide Web Browsing Patterns. Knowl. Inf. Syst. 1(1), 12–23 (1999)CrossRefGoogle Scholar
  28. 28.
    Etzioni, O.: The World Wide Web: Quagmire or gold mine? Communications of the ACM 39(11), 65–68 (1996)CrossRefGoogle Scholar
  29. 29.
    Han, J., Pei, J., Mortazavi-Asi, B., Chen, Q., Dayal, U., Hsu, M.C.: FreeSpan: Frequent Pattern-Projected Sequential Pattern Mining. In: SIGKDD 2000 (2000)Google Scholar
  30. 30.
    Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: ICDE (1995)Google Scholar
  31. 31.
    Fu, Y., Sandhu, K., Shih, M.Y.: Clustering of Web Users Based on Access Patterns. In: Proceedings of WEBKDD (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Oznur Kirmemis Alkan
    • 1
  • Pınar Karagoz
    • 1
  1. 1.Computer Engineering DepartmentMiddle East Technical University (METU)AnkaraTurkey

Personalised recommendations