1 |
S. Guha and N. Koudas, “Approximating a Data Stream for Querying and Estimation: Algorithms and Performance Evaluation,” In Proc. of the 18th Int'l Conf. on Data Engineering, pp.567-576, 2002
DOI
|
2 |
G. Dong, J. Han, L.V.S. Lakshmanan, J. Pei, H. Wang, and P.S. Yu. Online Mining of Changes from Data Streams: Research Problems and Preliminary Results. Proc. of the Workshop on Management and Processing of Data Streams, 2003
|
3 |
Wei-Guang Teng, Ming-Syan Chen, Philip S. Yu. A Regression-Based Temporal Pattern Mining Scheme for Data Streams, Proc. of the 29th Int'l Conf on Very Large Database, Berlin, Germany, 2003
|
4 |
R.C. Agarwal, C.C. Aggarwal, and V.V.V. Prasad, “Depth First Generation of Long Patterns,” In Proc. of the 6th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining, pp.108-118, 2000
DOI
|
5 |
C.C. Aggarwal and P.S. Yu, “Online Generation of Association Rules,” Proc. of the 14th Int'l IEEE Conf. on Data Engineering, pp.402-411, 1998
|
6 |
R. Agrawal, and R. Srikant. Fast algorithms for mining association rules. Proc. of the 20th Int'l Conf. on Very Large Databases, Santiago, Chile, Sept., 1994
|
7 |
Zhihong Chong, Jeffrey Xu Yu, Hongjun Lu, Zhengjie Zhang, and Aoying Zhou. False-Negative Frequent Items Mining from Data Streams with Bursting. Proc. of the 10th Int'l Conf on Database Systems for Advanced Applications, pp.422-434, 2005
DOI
|
8 |
L. Qiao, D. Agrawal, and A.E. Abbadi, “RHist: Adaptive Summarization over Continuous Data Streams,” Proc. of the 10th Int'l Conf. on Information and Knowledge Management, pp.469-476, 2002
|
9 |
A. Hafez, J. Deogun, and V. V. Raghavan. “The Item-Set Tree: A data Structure for Data Mining.” Proc. of the 1st int'l Conf on data warehousing and knowledge discovery, pp. 183-192, Aug., 1999
|
10 |
N. Jiang, and L. Gruenwald, “CFI-Stream: Mining Closed Frequent Itemsets in Data Streams,” Proc. of the 12th ACM SIGKDD int'l Conf. on Knowledge Discovery and Data Mining, pp.592-597, 2006
DOI
|
11 |
M. Garofalakis, J. Gehrke and R. Rastogi. “Querying and mining data streams: you only get one look”. In the tutorial notes of the 28th Int'l Conf. on Very Large Databases, 2002
|
12 |
J. H. Chang, W. S. Lee. “Finding recent frequent itemsets adaptively over online data streams.” In Proc. of the 9th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining, Washington, DC, 24-27, August, 2003
DOI
|
13 |
M.J. Zaki, “Generating Non-Redundant Association Rules,” In Proc. of the 6th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining, pp.34-43, 2000
DOI
|
14 |
M. Datar, A. Gionis, P. Indyk, and R. Motwani, “Maintaining Stream Statistics over Sliding Windows,” Proc. of the 13th Ann. ACM-SIAM Symp. Discrete Algorithms, pp.635-644, 2002
|
15 |
S. Brin, R. Motwani, J.D. Ullman, and S. Tsur, “Dynamic Itemset Counting and Implication Rules for Market Basket Data,” In Proc. of ACM SIGMOD Int'l Conf. Management of Data, pp.255-264, 1997
DOI
|
16 |
Yun Chi, Haixun Wang, Philip S. Yu, Richard R. Muntz “Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window.” In Proc. of the 4th IEEE int'l Conf. on Data Mining, pp.59-66, 2004
DOI
|
17 |
M. Charikar, K. Chen, and M. Farach-Colton, “Finding Frequent Items in Data Streams,” Proc. of the 29th Int'l. Colloq. Automata, Language and Programming, 2002
|
18 |
G.S. Manku and R. Motwani, “Approximate Frequency Counts over Data Streams,” Proc. of the 28th Int'l Conf. on Very Large Data Bases, 2002
|