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Mining Multiple-Level Association Rules in Large Databases
September/October 1999 (vol. 11 no. 5)
pp. 798-805

Abstract—A top-down progressive deepening method is developed for efficient mining of multiple-level association rules from large transaction databases based on the Apriori principle. A group of variant algorithms is proposed based on the ways of sharing intermediate results, with the relative performance tested and analyzed. The enforcement of different interestingness measurements to find more interesting rules, and the relaxation of rule conditions for finding “level-crossing” association rules, are also investigated in the paper. Our study shows that efficient algorithms can be developed from large databases for the discovery of interesting and strong multiple-level association rules.

[1] R. Agrawal, T. Imielinski, and A. Swami, “Mining Association Rules Between Sets of Items in Large Databases,” Proc. 1993 ACM-SIGMOD Int'l Conf. Management of Data, pp. 207-216, May 1993.
[2] R. Agrawal and J.C. Shafer, Parallel Mining of Association Rules: Design, Implementation, and Experience IEEE Trans. Knowledge and Data Eng., pp. 487-499, Dec. 1996.
[3] R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proc. 1994 Int'l Conf. Very Large Data Bases, pp. 487-499, Sept. 1994.
[4] R. Agrawal and R. Srikant, “Mining Sequential Patterns,” Proc. 1995 Int'l Conf. Data Eng., pp. 3-14, Mar. 1995.
[5] S. Brin, R. Motwani, and C. Silverstein, “Beyond Market Basket: Generalizing Association Rules to Correlations,” Proc. 1997 ACM-SIGMOD Int'l Conf. Management of Data, pp. 265-276, May 1997.
[6] S. Chaudhuri and U. Dayal, “An Overview of Data Warehousing and OLAP Technology,” SIGMOD Record, vol. 26, no. 1, Mar. 1997.
[7] M.-S. Chen, J. Han, and P.S. Yu, Data Mining: An Overview from Database Perspective IEEE Trans. Knowledge and Data Eng., vol. 8, no. 6, pp. 866-883, Dec. 1996.
[8] D. Cheung, J. Han, V. Ng, A. Fu, and Y. Fu, “A Fast Distributed Algorithm for Mining Association Rules,” Fourth Int'l Conf. Parallel and Distributed Information Systems, Dec. 1996.
[9] D. Cheung, J. Han, V. Ng, and C.Y. Wong, Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique Proc. 1996 Int'l Conf. Data Eng., pp. 106-114, Feb. 1996.
[10] Y. Fu and J. Han, “Meta-Rule-Guided Mining of Association Rules in Relational Databases,” Proc. First Int'l Workshop Integration Knowledge Discovery with Deductive and Object-Oriented Databases (KDOOD '95), pp. 39–46, Singapore, Dec. 1995.
[11] T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama, “Data Mining Using Two-Dimensional Optimized Association Rules: Scheme, Algorithms, and Visualization,” Proc. 1996 ACM-SIGMOD Int'l Conf. Management of Data, pp. 13-23, June 1996.
[12] J. Han, Y. Cai, and N. Cercone, "Data-Driven Discovery of Quantitative Rules in Relational Databases," IEEE Trans. Knowledge and Data Eng., pp. 29-40, Feb. 1993.
[13] J. Han and Y. Fu, “Mining Multiple-Level Association Rules in Large Databases,” technical report, Univ. of Missouri–Rolla, URL:http://www.umr.edu/~yongjian/pubml.ps, 1997.
[14] M. Houtsma and A. Swami, “Set-Oriented Mining of Association Rules in Relational Databases,” 11th Int'l Conf. Data Eng., 1995.
[15] M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A.I. Verkamo, “Finding Interesting Rules from Large Sets of Association Rules,” Proc. Third Int'l Conf. Information and Knowledge Management, N.R. Adam, K.B. Bhargava, and Y. Yesha, eds. pp. 401-407, 1994.
[16] K. Koperski and J. Han, “Discovery of Spatial Association Rules in Geographic Information Databases,” Proc. Fourth Int'l Symp. Large Spatial Databases (SSD '95), pp. 47–66, Portland, Maine, Aug. 1995.
[17] H. Mannila, H. Toivonen, and A.I. Verkamo, “Efficient Algorithms for Discovering Association Rules,” Proc. AAAI '94 Workshop Knowledge Discovery in Databases (KDD '94), pp. 181–192, Seattle, July 1994.
[18] R. Meo, G. Psaila, and S. Ceri, “A New SQL-Like Operator for Mining Association Rules,” Proc. 1996 Int'l Conf. Very Large Data Bases, pp. 122-133, Sept. 1996.
[19] J.S. Park, M.S. Chen, and P.S. Yu, “An Effective Hash-Based Algorithm for Mining Association Rules,” Proc. 1995 ACM-SIGMOD Int'l Conf. Management of Data, pp. 175-186, May 1995.
[20] G. Piatetsky-Shapiro, “Discovery, Analysis, and Presentation of Strong Rules,” Knowledge Discovery in Databases, G. Piatetsky-Shapiro and W.J. Frawley, eds., pp. 229–238, AAAI/MIT Press, 1991.
[21] A. Savasere, E. Omiecinski, and S. Navathe, “An Efficient Algorithm for Mining Association Rules in Large Databases,” Proc. 1995 Int'l Conf. Very Large Data Bases, pp. 432-443, Sept. 1995.
[22] Proc. Second Int'l Conf. Knowledge Discovery and Data Mining (KDD '96), E. Simoudis, J. Han, and U. Fayyad, eds., AAAI Press, Aug. 1996.
[23] R. Srikant and R. Agrawal, “Mining Generalized Association Rules,” Proc. 1995 Int'l Conf. Very Large Data Bases, pp. 407-419, Sept. 1995.
[24] R. Srikant and R. Agrawal, “Mining Quantitative Association Rules in Large Relational Tables,” Proc. 1996 ACM-SIGMOD Int'l Conf. Management of Data, pp. 1-12, June 1996.

Index Terms:
Data mining, knowledge discovery in databases, association rules, multiple-level association rules, algorithms, performance.
Citation:
Jiawei Han, Yongjian Fu, "Mining Multiple-Level Association Rules in Large Databases," IEEE Transactions on Knowledge and Data Engineering, vol. 11, no. 5, pp. 798-805, Sept.-Oct. 1999, doi:10.1109/69.806937
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