[1]
Agrawal, R. & Srikant, R. Fast algorithm for mining association rules. In the international conference on very large databases,1994,p.487–499.
Google Scholar
[2]
Han, J., Pei, J., & Yin, Y. Mining frequent patterns without candidate generation. In The 2000 ACM SIGMOD international conference on management of data,2000,p.1–12.
DOI: 10.1145/342009.335372
Google Scholar
[3]
Jiawei Han, Micheline Kamber. Data mining concepts and techniques. Beijing: machine press, 2002, pp.56-80.
Google Scholar
[4]
Lee D, Lee W. Finding maximal frequent itemsets over online data streams adaptively. In Proceedings of the fifth IEEE International Conference on data mining, Houston, Texas, USA, November 2005,pp.266-273
DOI: 10.1109/icdm.2005.68
Google Scholar
[5]
Li, H.-F., Lee, S.-Y. Approximate mining of maximal frequent itemsets in data streams with different window models. Expert systems with applications,35(2008)781-789
DOI: 10.1016/j.eswa.2007.07.046
Google Scholar
[6]
Chi Y, Wang H, Yu P, Muntz R. Moment: maintaining closed frequent itemsets over a stream sliding window. In the 4th IEEE international conference on data mining, Brighton, UK, November 2004, pp.59-66
DOI: 10.1109/icdm.2004.10084
Google Scholar
[7]
N.Jiang, L.Gruenwald. CFI-Stream: mining closed frequent itemsets in data streams. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, 2006, pp.592-597
DOI: 10.1145/1150402.1150473
Google Scholar
[8]
James Cheng, Yiping Ke, Wilfred Ng. Maintaining frequent closed itemsets over a sliding window. Journal of Intelligent Information Systems,31(2008)191-215
DOI: 10.1007/s10844-007-0042-3
Google Scholar
[9]
Albert Bifet, Ricard Gavalda. Mining adaptively frequent closed unlabeled rooted trees in data streams. In Proceedings of KDD'08,Las Vegas, Nevada, USA, August 2008,pp.34-42
DOI: 10.1145/1401890.1401900
Google Scholar
[10]
Li, H-F., Ho, C.-C., &Lee, S.-Y. Incremental updates of closed frequent itemsets over continuous data streams. Expert Systems with Applications, 36(2009)2451-2458
DOI: 10.1016/j.eswa.2007.12.054
Google Scholar
[11]
Yu J, Chong Z, Lu H, Zhou A. False positive or false negative: mining frequent itemsets from high speed transactional data streams. In the 13th international conference on very large data bases, Toronto, Canada, September 2004, pp.204-215
DOI: 10.1016/b978-012088469-8/50021-8
Google Scholar
[12]
Manku GS, Motwani R. Approximate frequency counts over data streams. In the 28th international conference on very large data bases, Hong Kong, August 2002,pp.346-357
DOI: 10.1016/b978-155860869-6/50038-x
Google Scholar
[13]
Li H, Lee S, Shan M. An efficient algorithm for mining frequent itemsets over the entire history of data streams. In the first international workshop on knowledge discovery in data streams, in conjunction with the 15th European conference on machine learning ECML and the 8th European conference on the principals and practice of knowledge discovery in databases PKDD, Pisa,Italy,(2004)
DOI: 10.1109/fskd.2010.5569199
Google Scholar
[14]
Chang J, Lee W. Finding recent frequent itemsets adaptively over online data streams. In the 9th ACM SIGKDD international conference on knowledge discovery and data mining, Washington, DC, August 2003, pp.487-492
DOI: 10.1145/956750.956807
Google Scholar
[15]
Giannella C, Han J, Pei J, Yan X, Yu P. Mining frequent patterns in data streams at multiple time granularities. In the ACM SIGMOD international conference on management, Wisconsin, June 2002, pp.635-645
Google Scholar
[16]
Chang J, Lee W. estWin: adaptively monitoring the recent change of frequent itemsets over online data streams. In the international conference on information and knowledge management, New Orleans, Louisiana, USA, November 2003, pp.536-539
DOI: 10.1145/956863.956967
Google Scholar
[17]
Chang J, Lee W. A sliding window method for finding recently frequent itemsets over online data streams[J]. Journal of information science and engineering, 20(4) (2004)753-762
DOI: 10.1016/j.is.2005.04.001
Google Scholar
[18]
Li, H.-F., &Lee,S.-Y. Mining frequent itemsets over data streams using efficient window sliding techniques[J]. Expert System with Applications, 36(2009)1466-1477
DOI: 10.1016/j.eswa.2007.11.061
Google Scholar
[19]
B.Mozafari, H.Thakkar, C.Zaniolo, Verifying and mining frequent patterns from large windows over data streams. In Proceedings of ICDE,2008,pp.179-188
DOI: 10.1109/icde.2008.4497426
Google Scholar
[20]
Richard M. Karp, Scott Shenker. A simple algorithm for finding frequent elements in streams and bags. ACM Transactions on database systems, March (2003)
DOI: 10.1145/762471.762473
Google Scholar