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Mining and Cyclic Behaviour Analysis of Web Sequential Patterns

  • K. R. VenugopalEmail author
  • K. C. Srikantaiah
Chapter
  • 44 Downloads

Abstract

Understanding Web users’ behaviour is an important criterion for improving the overall experience of Web users. Web Pattern Mining is one such field that helps us to mine useful behavioural patterns and draw conclusions from them after careful analysis. Efficient Web pattern mining is a challenge taking into consideration the enormous quantities of raw Web log data and explosive growth of information in the Web. In this chapter, we propose a novel algorithm called Bidirectional Growth based mining Cyclic Behaviour Analysis of Web sequential Patterns (BGCAP) that effectively combines these strategies to generate prefetching rules in the form of 2-sequence patterns with Periodicity and threshold of Cyclic Behaviour that can be utilized to effectively prefetch Web pages, thus reducing the users’ perceived latency. In other words, BGCAP grow patterns bidirectionally along both ends of detected patterns and allows faster pattern growth with fewer levels of recursion thus eliminating unnecessary candidates and support for efficient pruning of invalid candidates. Due to these facts, BGCAP requires only (log n+1) levels of recursion for mining n Web Sequential Patterns. Our experimental results show that the Web Sequential Patterns and in turn prefetching rules generated using BGCAP is 5–10% faster for different data sizes and generates about 5–15% more prefetching rules than TD-Mine.

References

  1. 1.
    C. Jinlin, An updown directed acyclic graph approach for sequential pattern mining. IEEE Trans. Knowl. Data Eng. 22(7), 913–928 (2010)CrossRefGoogle Scholar
  2. 2.
    D-A. Chiang, C-T. Wang, S-P. Chen, C-C. Chen, The cyclic model analysis on sequential patterns. IEEE Trans. Knowl. Data Eng. 21(11), 1617–1628 (2009)Google Scholar
  3. 3.
    Y. Hirate, H. Yamana, Generalized sequential pattern mining with item intervals. J. Comput. 1(3), 51–60 (2006)Google Scholar
  4. 4.
    M.J. Zaki, Spade: an efficient algorithm for mining frequent sequences. Machine Learning, vol. 42, pp. 31–60 (2001)Google Scholar
  5. 5.
    Z. Yang, M. Kitsuregawa, LAPIN-SPAM: an improved algorithm for mining sequential pattern, in IEEE International Conference on Data Engineering Workshops, pp. 1222–1226 (2005)Google Scholar
  6. 6.
    Z. Yang, Y. Wang, M. Kitsuregawa, LAPIN: effective sequential pattern mining algorithms by last position induction for dense databases, in International Conference on Database Systems for Advanced Applications, pp. 1020–1023 (2007)Google Scholar
  7. 7.
    J. Wang, J. Han, C. Li, Frequent closed sequence mining without candidate maintenance. IEEE Trans. Knowl. Data Eng. 19(8), 1042–1056 (2007)Google Scholar
  8. 8.
    K-Y. Huang, C-H. Chang, J-H. Tung, C-T. Ho.: COBRA: closed sequential pattern mining using bi-phase reduction approach, in International Conference on Data Warehousing and Knowledge Discovery, pp. 280–291 (2006)Google Scholar
  9. 9.
    J. Pei, J. Han, B. Mortazavi-Asl, J. Wang, H. Pinto, Q. Chen, U. Dayal, M-C. Hsu, Mining sequential patterns by pattern-growth: the prefixspan approach. IEEE Trans. Knowl. Data Eng. 16(11), 1424–1440 (2004)Google Scholar
  10. 10.
    X. Cheng, H. Liu, Personalized services research based on web data mining technology, in IEEE International Symposium on Computational Intelligence and Design, pp. 177–180 (2009)Google Scholar
  11. 11.
    F. Lumban Gaol, Exploring the pattern of habits of users using web log sequential pattern, in IEEE International Conference on Advances in Computing, Control and Telecommunication Technologies, pp. 161–163 (2010)Google Scholar
  12. 12.
    J. Pei, J. Han, B. Mortazavi-asl, H. Zhu, Mining access patterns efficiently from web logs, in Pacific-Asia Conference on Knowledge Discovery and Data Mining Current Issues and New Applications, pp. 396–407 (2000)Google Scholar
  13. 13.
    T. Xiaoqiu, Y. Min, Z. Jianke, Mining maximal frequent access sequences based on improved WAP-tree, in IEEE International Conference on Intelligent Systems Design and Applications, pp. 616–620 (2006)Google Scholar
  14. 14.
    S. Yang, J. Guo, Y. Zhu, An efficient algorithm for web access pattern mining, in Fourth International Conference on Fuzzy Systems and Knowledge Discovery, pp. 726–729 (2007)Google Scholar
  15. 15.
    L. Liu, J. Liu, Mining web log sequential patterns with layer coded breadth-first linked WAP-tree, in IEEE International Conference on Information Science and Management Engineering, pp. 28–31 (2010)Google Scholar
  16. 16.
    V. Mohan, S. Vijayalakshmi, S. Suresh Raja, Mining constraint-based multidimensional frequent sequential pattern in web logs. Eur. J. Sci. Res. 36(3), 480–490 (2009)Google Scholar
  17. 17.
    H-Y. Wu, J-J. Zhu, X-Y. Zhang, the explore of the web-based learning environment based on web sequential pattern mining, in IEEE International Conference on Computational Intelligence and Software Engineering, pp. 1–6 (2009)Google Scholar
  18. 18.
    B. Verma, K. Gupta, S. Panchal, R. Nigam, Single level algorithm: an improved approach for extracting user navigational patterns to technology, in International Conference on Computer and Communication Technology, pp. 436–441 (2010)Google Scholar
  19. 19.
    O. Nasraoui, M. Soliman, E. Saka, A. Badia, R. Germain, A web usage mining framework for mining evolving user profiles in dynamic web sites. IEEE Trans. Knowl. Data Eng. 20(2), 202–215 (2008)Google Scholar
  20. 20.
    A. Pitman, M. Zanker, Insights from applying sequential pattern mining to E-commerce click stream data, in IEEE International Conference on Data Mining Workshops, pp. 967–975 (2010)Google Scholar
  21. 21.
    F. Masseglia, M. Teisseire, Pascal poncelet.: real time web usage mining with a distributed navigation analysis, in International Workshop on Research Issues in Data Engineering, pp. 169–174 (2002)Google Scholar
  22. 22.
    B. Zhou, S.C. Hui, K. Chang, An intelligent recommender system using sequential web access patterns, in IEEE Conference on Cybernetics and Intelligent Systems, pp. 393–398 (2004)Google Scholar
  23. 23.
    S-J. Yen, Y-S. Lee, M-C. Hsieh, An efficient incremental algorithm for mining web traversal patterns, in IEEE International Conference on e-Business Engineering, pp. 274–281 (2005)Google Scholar
  24. 24.
    Z. Zhang, X. Qian, Y. Zhao, Galois lattice for web sequential patterns mining, in IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, pp. 102–106 (2008)Google Scholar
  25. 25.
    S. Jain, R.K. Jain, R.S. Kasana, Efficient web log mining using doubly linked tree. Int. J. Comput. Sci. Inf. Sec. 3(1), 1–5 (2009)Google Scholar
  26. 26.
    D.K. Jha, A. Rajput, M. Singh, A. Tomar, An efficient model for information gain of sequential pattern from web logs based on dynamic weight constraint, in IEEE International Conference on Computer Information Systems and Industrial Management Applications, pp. 518–523 (2010)Google Scholar
  27. 27.
    X. Wang, Y. Bai, Y. Li, An information retrieval method based on sequential access patterns, in IEEE Asia-Pacific Conference on Wearable Computing Systems, pp. 247–250 (2010)Google Scholar
  28. 28.
    K. Saxena, R. Shukla, Significant interval and frequent pattern discovery in web log data. IJCSI Int. J. Comput. Sci. Issues 7(3), 29–36 (2010)Google Scholar
  29. 29.
    D. Oikonomopoulou, M. Rigou, S. Sirmakessis, A. Tsakalidis, Full-web prediction based on web usage mining and site topology, in IEEE/WIC/ACM International Conference on Web Intelligence, pp. 716–719 (2004)Google Scholar
  30. 30.
    A. Rajimol, G. Raju, Mining maximal web access patterns- a new approach. Int. J. Mach. Intell. 3(4), 346–348 (2011)Google Scholar
  31. 31.
    K.C. Srikantaiah, N. Krishnakumar, K.R. Venugopal, L.M. Patnaik, Web caching and prefetching with cyclic model analysis of web object sequences. Int. J. Knowl. Web Intell. 5(1), 76–103 (2014)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Bangalore UniversityBengaluruIndia
  2. 2.SJB Institute of TechnologyKengeri, BengaluruIndia

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