中文部份
陳柏宏(2007)。針對時空性交易資料提供動態視窗關聯法則探勘之研究,國立台南大學數位學習科技研究所碩士論文,未出版。英文部分
[1]R. Agrawal, T. Imielinski, and A. Swami, "Mining association rules between sets of items in large databases," ACM SIGMOD Record, vol. 22, pp. 207-216, 1993.
[2]R. Agrawal and R. Srikant, "Fast algorithms for mining association rules," Proc. 20th Int. Conf. Very Large Data Bases, VLDB, vol. 1215, pp. 487-499, 1994.
[3]J. H. Chang and W. S. Lee, "Finding recent frequent itemsets adaptively over online data streams," Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 487-492, 2003.
[4]J. H. Chang and W. S. Lee, "A Sliding Window Method for Finding Recently Frequent Itemsets over Online Data Streams," Journal of Information Science and Engineering, vol. 20, pp. 753-762, 2004.
[5]M. Charikar, K. Chen, and M. Farach-Colton, "Finding frequent items in data streams," Theoretical Computer Science, vol. 312, pp. 3-15, 2004.
[6]Y. Chi, H. Wang, P. S. Yu, and R. R. Muntz, "Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window," Proc. of, vol. 1401, 2004.
[7]G. Cormode and S. Muthukrishnan, "What''s hot and what''s not: tracking most frequent items dynamically," ACM Transactions on Database Systems (TODS), vol. 30, pp. 249-278, 2005.
[8]M. M. Gaber, A. Zaslavsky, and S. Krishnaswamy, "Mining data streams: a review," ACM SIGMOD Record, vol. 34, pp. 18-26, 2005.
[9]C. Giannella, J. Han, J. Pei, X. Yan, and P. S. Yu, "Mining Frequent Patterns in Data Streams at Multiple Time Granularities," Next Generation Data Mining, vol. 212, 2003.
[10]J. Han, J. Pei, Y. Yin, and R. Mao, "Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach," Data Mining and Knowledge Discovery, vol. 8, pp. 53-87, 2004.
[11]N. Jiang, "CFI-Stream: mining closed frequent itemsets in data streams," Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 592-597, 2006.
[12]N. Jiang, "Research issues in data stream association rule mining," ACM SIGMOD Record, vol. 35, pp. 14-19, 2006.
[13]C. Jin, W. Qian, C. Sha, J. X. Yu, and A. Zhou, "Dynamically maintaining frequent items over a data stream," Proceedings of the twelfth international conference on Information and knowledge management, pp. 287-294, 2003.
[14]W. Jinlong, X. Congfu, C. Weidong, and P. Yunhe, "Survey of the study on frequent pattern mining in data streams," Systems, Man and Cybernetics, 2004 IEEE International Conference on, vol. 6, 2004.
[15]R. M. Karp, S. Shenker, and C. H. Papadimitriou, "A simple algorithm for finding frequent elements in streams and bags," ACM Transactions on Database Systems (TODS), vol. 28, pp. 51-55, 2003.
[16]J. L. Koh and S. N. Shin, "An Approximate Approach for Mining Recently Frequent Itemsets from Data Streams," presented at DaWaK, 2006.
[17]C. K. S. Leung and Q. I. Khan, "DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams," Proceedings of the Sixth International Conference on Data Mining, pp. 928-932, 2006.
[18]H. F. Li, C. C. Ho, F. F. Kuo, and S. Y. Lee, "A New Algorithm for Maintaining Closed Frequent Itemsets in Data Streams by Incremental Updates," Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on, pp. 672-676, 2006.
[19]H. F. Li, S. Y. Lee, and M. K. Shan, "An efficient algorithm for mining frequent itemsets over the entire history of data streams," Proc. of the 1st Intl. Workshop on Knowledge Discovery in Data Streams, 2004.
[20]G. S. Manku and R. Motwani, "Approximate frequency counts over data streams," Proc. VLDB, vol. 2, pp. 346–357, 2002.
[21]G. Mao, X. Wu, C. Liu, X. Zhu, G. Chen, Y. Sun, and X. Liu, "Online Mining of Maximal Frequent Itemsequences from Data Streams," 2005.
[22]L. Yang and M. Sanver, "Mining Short Association Rules with One Database Scan," Int''l, 2004.
[23]J. X. Yu, Z. Chong, H. Lu, and A. Zhou, "False Positive or False Negative: Mining Frequent Itemsets from High Speed Transactional Data Streams," Proceedings of the 30th ACM VLDB International Conference on Very Large Data Bases, pp. 204–215, 2004.