Open Access
ITM Web Conf.
Volume 12, 2017
The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
Article Number 03024
Number of page(s) 4
Section Session 3: Computer
Published online 05 September 2017
  1. Yin J, Zheng Z, Cao L. Uspan: an efficient algorithm for mining high utility sequential patterns. Proceedings of the 18th ACM SIGKDD. 2012. [Google Scholar]
  2. J. Yin, Z. Zheng, L. Cao, Y. Song, W. Wei. Efficiently mining Top-k high utility sequential patterns. 2013 IEEE 13th International Conference on Data Mining (ICDM 2013). 2013. [Google Scholar]
  3. Wang Le, Wang shui, Liu Sheng-lan, Wang Hui-bing. An algorithm of Mining Sequential pattern with wildcards based on Index-Tree[J]. Chinese Journal of Computers. 2016.11, 39(178). [EDP Sciences] [Google Scholar]
  4. Wu Xindong, Xie Fei, Huang Yongming, Hu Xuegang, Gao Jun. Mining sequential patterns with wildcards and One-Off conditions[J]. Journal Of Software. 2013, 24(8). [Google Scholar]
  5. CHENG Si-Yuan, MA Chao, LI Cong-Cong. High Utility Sequential Pattern Mining Algorithm Based on MapReduce .Computer Systems & Applications. 2015, 24(12). [Google Scholar]
  6. Zaki M J. SPADE: an efficient algorithm for mining frequent sequences[J]. Machine Learning, 2001, 42 (1/2): 31–60. [CrossRef] [Google Scholar]
  7. Ceddia J, Sheard J, Tibbey G. WAT: a tool for classifying learning activities from a log file[C]/ / Proceedings of the 9th Australasian Conference on Computing Education. Darling Hurst: Australian Computer Society, 2007. [Google Scholar]
  8. Liang Q A, Miller S, Chung J. Service mining for Web service composition[C]/ / IEEE International Conference on In Formation Reuse and Integration. Las Vegas, Nevada, 2005. [Google Scholar]
  9. Han J W, Kamber M. Data Mining: Concept s and Techniques [M]. The 2nd editor. San Francisco: Morgan Kaufmann Publishers, 2006. [Google Scholar]
  10. Asbagh M J, Abolhassani H. Web service usage mining: mining for executable sequences [C]/ / Proceedings of the 7th WSEAS International Conference on Applied Computer Science. Wisconsin: World Scientific and Engineering Academy and Society, 2007. [Google Scholar]
  11. Lin M Y, Hsueh S C, Chang C W. Fast discovery of sequential patterns in large databases using effective time-indexing[J]. Information Sciences, 2008, 178 (22): 4228–4245. [CrossRef] [MathSciNet] [Google Scholar]
  12. Silvestri C, Orlando S. Approximate mining off frequent patterns on streams [J]. Intelligent Data Analysis, 2007, 11 (1). [Google Scholar]
  13. Mulvenna M D, Anand S S, Büchner A G. Personalization on the net using Web mining [J]. Communications of ACM, 2000, 43 (8):1222–1251. [CrossRef] [Google Scholar]
  14. CAI Hong-guo, YUAN Chang-an, etc. Server Session Constraint-based Serial Pattern Growth Mining Research. The journey of Zhengzhou University. 2010, 42(1). [Google Scholar]
  15. J Pei, J Han, B Mortazavi-Asl, H Pinto, Q Chen. PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. International Conference on Data Engineering, 2001:215–224. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.