Open Access
| Issue |
ITM Web Conf.
Volume 12, 2017
The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
|
|
|---|---|---|
| Article Number | 01002 | |
| Number of page(s) | 4 | |
| Section | Session 1: Robotics | |
| DOI | https://doi.org/10.1051/itmconf/20171201002 | |
| Published online | 05 September 2017 | |
- Yakop, M.A.M., S. Mutalib and S. Abdul-Rahman, Data Projection Effects in Frequent Itemsets Mining. 2015: Springer Singapore. 23–32. [Google Scholar]
- Malviya, J., A. Singh and D. Singh, An FP Tree based Approach for Extracting Frequent Pattern from Large Database by Applying Parallel and Partition Projection. International Journal of Computer Applications, 2015. 114(18): p. 1–5. [CrossRef] [Google Scholar]
- Deepak, A., et al., EvoMiner: frequent subtree mining in phylogenetic databases. Knowledge and Information Systems, 2014. 41(3): p. 559–590. [CrossRef] [Google Scholar]
- Wu Qian, Luo Jianxu, An Improved Search Algorithm for Compressed FP-Tree.Computer Engineering and Design, 2015 (7): 1771–1777. [EDP Sciences] [Google Scholar]
- Feng, B., et al. A new method of mining frequent closed trees in data streams. in International Conference on Fuzzy Systems and Knowledge Discovery, Fskd 2010, 10-12 August 2010, Yantai, Shandong, China. 2010. [Google Scholar]
- Yang Pei, Tan Qi, Maximal Frequent Subtree Mining and its Application, in Computer Science, 2008.35 (2): 150–153. [Google Scholar]
- D. Xin, J. Han, X. Yan, et al. Mining Compressed Frequent Pattern Sets[C]//31th International Conference on Very Large Data Bases (VLDB). Santiago de Chile,Chile:VLDB Endowment,2005:709–720. [Google Scholar]
- Liu, L. and J. Liu. Mining Frequent Embedded Subtree From Tree-Like Databases. in International Conference on Internet Computing & Information Services. 2011. [Google Scholar]
- A. K. Jain, R. C. Dubes. Algorithms for Clustering Data[M]. 1st ed., Prentice Hall,1998:366–367 [EDP Sciences] [Google Scholar]
- Han, K., et al. Constrained Frequent Subtree Mining Method. in International Conference on Digital Home. 2014. [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.