[1]
Pan Yun-He, WangJin-Long, Xu Cong-Fu. State-of-the-art on Frequent Pattern Mining in Data Streams[J]. Acta Autom atica Sinica, 2007, 32(4): 594-602.
Google Scholar
[2]
Giannella C, Han J, Pei J, et al. Mining frequent patterns in data streams at multiple time granularities. In: Data Mining: Next Generation Challenges and Future Directions. 2004. 191~212.
Google Scholar
[3]
Han J, Pei J, Yin Y. Mining frequent patterns without candidate generation. In: Chen W, Naughton JF, Bernstein PA, eds. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. Dallas: ACM Press, 2000. 1~12.
DOI: 10.1145/342009.335372
Google Scholar
[4]
Junrui Yang, Yunkai Guo, Nanyan Liu. Fast Mining Maximal Frequent Itemsets Based On Sorted FP-Tree. In: Proceedings of the 7th World Congress on Intelligent Control and Automation. Chongqing, China: IEEE Press, 2008: 5391~5395.
DOI: 10.1109/wcica.2008.4593808
Google Scholar
[5]
Chi Y, Wang H, Yu P, Muntz R. MOMENT: maintaining closed frequent itemsets over a stream sliding window. In: Proceedings of the 2004 IEEE International Conference on Data Mining . Brighton, UK: IEEE Computer Society Press, 2004: 59~66.
DOI: 10.1109/icdm.2004.10084
Google Scholar
[6]
Manku GS, Motwani R. Approximate frequency counts over data streams. In: Bernstein P, Ioannidis Y, Ramakrishnan R, eds. Proceedings of the 28th International Conference on Very Large Data Bases. Hong Kong: Morgan Kaufmann, 2002. 346~357.
DOI: 10.1016/b978-155860869-6/50038-x
Google Scholar
[7]
Domingos P, Hulten G, Spencer L. Mining time-changing data streams. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco: ACM Press, 2001. 97~106.
DOI: 10.1145/502512.502529
Google Scholar