Journal of Engineering and Applied Sciences

Year: 2017
Volume: 12
Issue: 4
Page No. 857 - 863

Frequent Pattern Mining in Data Streams

Authors : Shirin Mirabedini, Mahdi Ahmadi Panah and Maryam Darbanian

References

Agrawal, R., T. Imielinski and A. Swami, 1993. Mining association rules between sets of items in large databases. Acm. SIGMOD. Rec., 22: 207-216.
CrossRef  |  Direct Link  |  

Chang, J. and W. Lee, 2003. Finding recent frequent itemsets adaptively over online data streams. Proceedings of the International Conference on Knowledge Discovery and Data Mining, August 24-27, 2003, Washington, DC., USA., pp: 487-492.

Chang, J.H. and W.S. Lee, 2005. estWin: Online data stream mining of recent frequent itemsets by sliding window method. Inform. Sci., 31: 76-90.
CrossRef  |  Direct Link  |  

Chen, G. and Q. Wei, 2002. Fuzzy association rules and the extended mining algorithms. Inf. Sci., 147: 201-228.
Direct Link  |  

Chi, Y., H. Wang, P.S. Yu and R.R. Muntz, 2006. Catch the moment: Maintaining closed frequent itemsets over a data stream sliding window. Knowledge Inform. Syst., 10: 265-294.
CrossRef  |  Direct Link  |  

Gaber, M.M., A. Zaslavsky and S. Krishnaswamy, 2005. Mining data streams: A review. ACM. Sigmod Rec., 34: 18-26.
CrossRef  |  Direct Link  |  

Giannella, C., J. Han, J. Pei, X. Yan and P.S. Yu, 2003. Mining Frequent Patterns in Data Streams at Multiple Time Granularities. In: Next Generation Data Mining, Kargupta, H., A. Joshi, K. Sivakumar and Y. Yesha (Eds.). CRC Press, USA., pp: 191-212.

Han, J., H. Cheng, D. Xin and X. Yan, 2007. Frequent pattern mining: Current status and future directions. Data Ming Knowl. Discovery, 15: 55-86.
CrossRef  |  

Han, J., J. Pei and Y. Yin, 2000. Mining frequent patterns without candidate generations. ACM SIGMOD Record, 29: 1-12.
CrossRef  |  

Hsu, P.Y., Y.L. Chen and C.C. Ling, 2004. Algorithms for mining association rules in bag databases. Inf. Sci., 166: 31-47.
Direct Link  |  

Hu, T., S.Y. Sung, H. Xiong ans Q. Fu, 2008. Discovery of maximum length frequent itemsets. Inf. Sci., 178: 69-87.
Direct Link  |  

Jiang, N. and L. Gruenwald, 2006. Research issues in data stream association rule mining. ACM. SIGMOD. Rec., 35: 14-19.
CrossRef  |  Direct Link  |  

Lee, A.J. and C.S. Wang, 2007. An efficient algorithm for mining frequent inter-transaction patterns. Inf. Sci., 177: 3453-3476.
Direct Link  |  

Lee, A.J., R.W. Hong, W.M. Ko, W.K. Tsao and H.H. Lin, 2007. Mining spatial association rules in image databases. Inf. Sci., 177: 1593-1608.
Direct Link  |  

Lee, Y.S. and S.J. Yen, 2008. Incremental and interactive mining of web traversal patterns. Inform. Sci., 178: 287-306.
CrossRef  |  Direct Link  |  

Leung, C.K.S. and Q.I. Khan, 2006. DSTree: A tree structure for the mining of frequent sets from data streams. Proceedings of the 6th International Conference on Data Mining (ICDM 06), December 18-22, 2006, IEEE, Canada, ISBN:0-7695-2701-7, pp: 928-932.

Leung, C.K.S. and Q.I. Khan, 2006. Efficient mining of constrained frequent patterns from streams. Proceedings of the 10th International Symposium on Database Engineering and Applications (IDEAS'06), December 11-14, 2006, IEEE, Canada, ISBN:0-7695-2577-6, pp: 61-68.

Li, H.F. and S.Y. Lee, 2009. Mining frequent itemsets over data streams using efficient window sliding techniques. Expert Syst. Applic., 36: 1466-1477.
CrossRef  |  Direct Link  |  

Li, J., D. Maier, K. Tufte, V. Papadimos and P.A. Tucker, 2005. No pane, no gain: Efficient evaluation of sliding-window aggregates over data streams. ACM. SIGMOD. Rec., 34: 39-44.
CrossRef  |  Direct Link  |  

Lin, C.H., D.Y. Chiu, Y.G. Wu and A.L.P. Chen, 2005. Mining frequent itemsets from data streams with a time-sensitive sliding window. Proc. SIAM Int. Conf. Data Min., 119: 68-79.

Manku, G.S. and R. Motwani, 2002. Approximate frequency counts over data streams. Proceedings of the 28th International Conference on Very Large Databases, August 20-23, 2002, New York, USA., pp: 346-357.

Mozafari, B., H. Thakkar and C. Zaniolo, 2008. Verifying and mining frequent patterns from large windows over data streams. Proceedings of the IEEE 24th International Conference on Data Engineering (ICDE 2008), April 7-12, 2008, IEEE, Los Angeles, California, ISBN:978-1-4244-1836-7, pp: 179-188.

Shen, L., H. Shen and L. Cheng, 1999. New algorithms for efficient mining of association rules. Inf. Sci., 118: 251-268.
Direct Link  |  

Silvestri, C. and S. Orlando, 2007. Approximate mining of frequent patterns on streams. Intell. Data Anal., 11: 49-73.
Direct Link  |  

Tanbeer, S.K., C.F. Ahmed, B.S. Jeong and Y.K. Lee, 2008. Efficient single-pass frequent pattern mining using a prefix-tree. Inform. Sci., 179: 559-583.
CrossRef  |  

Tsay, Y.J. and Y.W.C. Chien, 2004. An efficient cluster and decomposition algorithm for mining association rules. Inf. Sci., 160: 161-171.
Direct Link  |  

Tsay, Y.J., T.J. Hsu and J.R. Yu, 2009. FIUT: A new method for mining frequent itemsets. Inf. Sci., 179: 1724-1737.
Direct Link  |  

Wang, C.Y., S.S. Tseng and T.P. Hong, 2006. Flexible online association rule mining based on multidimensional pattern relations. Inf. Sci., 176: 1752-1780.
Direct Link  |  

Wang, F.H., 2008. On discovery of soft associations with most fuzzy quantifier for item promotion applications. Inf. Sci., 178: 1848-1876.
Direct Link  |  

Ye, F.Y., J.D. Wang and B.L. Shao, 2005. New algorithm for mining frequent itemsets in sparse database. Proceedings of the 2005 International Conference on Machine Learning and Cybernetics, Vol. 3, August 18-21, 2005, IEEE, Nanjing, China, ISBN:0-7803-9091-1, pp: 1554-1558.

Yu, J.X., Z. Chong, H. Lu and A. Zhou, 2004. False positive or false negative: Mining frequent itemsets from high speed transactional data streams. Proceedings of the 30th International Conference on Very Large Data Bases, Aug. 31-Sept. 3, Toronto, Canada, pp: 204-215.

Yu, J.X., Z. Chong, H. Lu, Z. Zhang and A. Zhou, 2006. A false negative approach to mining frequent itemsets from high speed transactional data streams. Inf. Sci., 176: 1986-2015.
Direct Link  |  

Zhang, S., J. Zhang and C. Zhang, 2007. EDUA: An efficient algorithm for dynamic database mining. Inf. Sci., 177: 2756-2767.
Direct Link  |  

Zhi-Jun, X., C. Hong and C. Li, 2006. An efficient algorithm for frequent itemset mining on data streams. Proceedings of the Industrial Conference on Data Mining (ICDM) 2006, July 14-15, 2006, Springer, Leipzig, Germany, pp: 474-491.

Zhu, Y. and D. Shasha, 2002. StatStream: Statistical monitoring of thousands of data streams in real time. Proceeding of the 28th International Conference on Very Large Data Bases, August 20-23, 2002, Hong Kong, China, pp: 358-369.

Design and power by Medwell Web Development Team. © Medwell Publishing 2024 All Rights Reserved