Table of Contents Author Guidelines Submit a Manuscript
Applied Computational Intelligence and Soft Computing
Volume 2012 (2012), Article ID 593147, 7 pages
Research Article

An Application of Improved Gap-BIDE Algorithm for Discovering Access Patterns

1Database and Bioinformatics Laboratory, Chungbuk National University, Cheongju 361-763, Republic of Korea
2Division of Science and Technology, BNU-HKBU United International College, Zhuhai 519-085, China
3Multimedia Systems Laboratory, School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Fukushima 965-8580, Japan

Received 9 March 2012; Revised 14 May 2012; Accepted 20 May 2012

Academic Editor: Qiangfu Zhao

Copyright © 2012 Xiuming Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. L. K. J. Grace, V. Maheswari, and D. Nagamalai, “Analysis of web logs and web user in web mining,” International Journal of Network Security & Its Applications, vol. 3, no. 1, 2011. View at Google Scholar
  2. K. Saxena and R. Shukla, “Significant interval and frequent pattern discovery in web log data,” International Journal of Computer Science Issue, vol. 7, no. 1, 2010. View at Google Scholar
  3. K. Suresh and S. Paul, “Distributed linear programming for weblog data using mining techniques in distributed environment,” International Journal of Computer Applications (0975–8887), vol. 11, no. 7, 2010. View at Google Scholar
  4. Y. Wang, J. Le, and D. Huang, “A method for privacy preserving mining of association rules based on web usage mining,” in International Conference on Web Information Systems and Mining (WISM '10), vol. 1, pp. 33–37, IEEE Computer Society Washington, Washington, DC, USA, 2010.
  5. C. Wei, W. Sen, Z. Yuan, and L. C. Chang, “Algorithm of mining sequential patterns for web personalization services,” ACM SIGMIS Database, vol. 40, no. 2, pp. 57–66, 2009. View at Publisher · View at Google Scholar
  6. J. Zhu, H. Wu, and G. Gao, “An efficient method of web sequential pattern mining based on session filter and transaction identification,” Journal of Networks, vol. 5, no. 9, pp. 1017–1024, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. X. Yu, M. Li, and H. Kim, “Mining access patterns using temporal interval relational rules from web logs,” in Proceedings of the 4th International Conference (FITAT/DBMI '11), pp. 80–83, 2011.
  8. M. Santini, “Cross-testing a genre classification model for the web,” Genres on the Web, vol. 42, Part 3, pp. 87–128, 2011. View at Google Scholar
  9. J. J. Rho, B. J. Moon, Y. J. Kim, and D. H. Yang, “Internet customer segmentation using web log data,” Journal of Business & Economics Research, vol. 2, no. 11, 2004. View at Google Scholar
  10. N. Kejžar, S. K. Èerne, and V. Batagelj, “Network analysis of works on clustering and classification from web of science,” in Proceedings of the 11th Conference of the International Federation of Classification Societies (IFCS '10), Part 3, pp. 525–536, 2010.
  11. G. Xu, Y. Zong, and P. Dolog, “Co-clustering analysis of weblogs using bipartite spectral projection approach,” in Proceedings of the 14th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES '10), vol. 6278, pp. 398–407, 2010.
  12. A. A. O. Makanju, A. N. Zincir-Heywood, and E. E. Milios, “Clustering event logs using iterative partitioning,” in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '09), pp. 1255–1263, July 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. J. Wang, Y. Mo, B. Huang, and J. Wen, “Web search results clustering based on a novel suffix tree structure,” in Proceedings of the 5th International Conference on Autonomic and Trusted Computing (ATC '08), vol. 5060, pp. 540–554, 2008.
  14. J. Chen and T. Cook, “Mining contiguous sequential patterns from web logs,” in Proceedings of the 16th International World Wide Web Conference (WWW '07), pp. 1177–1178, May 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. M. Saravanan and B. Valaramathi, “Generalization of web log datas using WUM technique,” in Proceedings of the 12th International Conference on Networking, VLSI and signal processing (ICNVS '10), pp. 157–165, 2010.
  16. N. R. Mabroukeh and C. I. Ezeife, “A taxonomy of sequential pattern mining algorithms,” ACM Computing Surveys, vol. 43, no. 1, article 3, 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. S. Ramakrishnan and A. Rakesh, “Mining sequential patterns: generalizations and performance improvements,” Lecture Notes in Computer Science, vol. 1057, pp. 3–17, 1996. View at Google Scholar
  18. J. Wang, J. Han, and C. Li, “Frequent closed sequence mining without candidate maintenance,” IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. 8, pp. 1042–1056, 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. C. Li and J. Wang, “Efficiently mining closed subsequences with gap constraints,” in Proceedings of International Conference on Data Mining (SIAM '08), April 2008. View at Scopus
  20. X. Yu, M. Li, D. G. Lee, K. D. Kim, and K. H. Ryu, “Application of closed gap-constrained sequential pattern mining in web log data,” in Proceedings of the 2nd International Conference of Electrical and Electronics Engineering (ICEEE '11), pp. 649–657, 2011.
  21. X. Yu, M. Li, H. Kim, D. G. Lee, and K. H. Ryu, “A novel approach to mining access patterns,” in Proceedings of the 3rd International Conference on Awareness Science and Technology, pp. 346–352, 2011.