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Sam Y. Sung, Zhao Li, Chew L. Tan, Peter A. Ng, "Forecasting Association Rules Using Existing Data Sets," IEEE Transactions on Knowledge and Data Engineering, vol. 15, no. 6, pp. 14481459, November/December, 2003.  
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@article{ 10.1109/TKDE.2003.1245284, author = {Sam Y. Sung and Zhao Li and Chew L. Tan and Peter A. Ng}, title = {Forecasting Association Rules Using Existing Data Sets}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {15}, number = {6}, issn = {10414347}, year = {2003}, pages = {14481459}, doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2003.1245284}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Knowledge and Data Engineering TI  Forecasting Association Rules Using Existing Data Sets IS  6 SN  10414347 SP1448 EP1459 EPD  14481459 A1  Sam Y. Sung, A1  Zhao Li, A1  Chew L. Tan, A1  Peter A. Ng, PY  2003 KW  Combination data set KW  data mining KW  extending association rule KW  fine partition KW  proportionate sampling. VL  15 JA  IEEE Transactions on Knowledge and Data Engineering ER   
Abstract—An important issue that needs to be addressed when using
[1] R. Agrawal, T. Imielinski, and A. Swami, “Mining Association Rules Between Sets of Items in Large Databases,” Proc. 1993 ACMSIGMOD Int'l Conf. Management of Data, pp. 207216, May 1993.
[2] K. Ali, S. Manganaris, and R. Srikant, Partial Classification Using Association Rules Proc. Third Int'l Conf. Knowledge Discovery and Data Mining, 1997.
[3] R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proc. 1994 Int'l Conf. Very Large Data Bases, pp. 487499, Sept. 1994.
[4] R. Agrawal and J. Shafer, Parallel Mining of Association Rules: Design, Implementation and Experience IEEE Trans. Knowledge and Data Eng., 1996.
[5] S. Brin, R. Motwani, J. Ullman, and S. Tsur, “Dynamic Itemset Counting and Implication Rules for Market Basket Data,” ACM SIGMOD Conf. Management of Data, May 1997.
[6] S. Brin, R. Motwani, and C. Silverstein, “Beyond Market Basket: Generalizing Association Rules to Correlations,” Proc. 1997 ACMSIGMOD Int'l Conf. Management of Data, pp. 265276, May 1997.
[7] G.F. Cooper, A Simple ConstraintBased Algorithm for Efficiently Mining Observational Databases for Causal Relationships Data Mining and Knowledge Discovery, vol. 1, no. 2, 1997.
[8] M. Dash, H. Liu, and J. Yao, Dimensionality Reduction of Unsupervised Data Proc. Ninth IEEE Int'l Conf. Tools with Artificial Intelligence (ICTAI '97), pp. 532539, 1997.
[9] J.D. Fast, Entropy: The Significance of the Concept of Entropy and Its Applications in Science and Technology The Statistical Significance of the Entropy Concept, Eindhoven: Philips Technical Library, 1962.
[10] Y. Fu and J. Han, MetaRuleGuided Mining of Association Rules in Relational Databases Proc. 1995 Int'l Workshop Knowledge Discovery and Deductive and ObjectOriented Databases, pp. 3946, Dec. 1995.
[11] T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama, Mining Optimized Association Rules for Numeric Attributes Proc. 1996 ACM Symp. Principles of Database Systems, pp. 182191, 1996.
[12] T. Fukuda, Y. Morimoto, S. Morishira, and T. Tokuyama, Constructing Efficient Decision Trees by Using Optimized Numeric Association Rules Proc. 22nd Int'l Conf. Very Large Databases, Dec. 1996.
[13] J. Han and Y. Fu, “Discovery of MultipleLevel Association Rules from Large Databases,” Proc. 1995 Int'l Conf. Very Large Data Bases, pp. 420431, Sept. 1995.
[14] 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. 401407, 1994.
[15] H. Mannila, H. Toivonen, and A. Verkamo, Efficient Algorithms for Discovering Association Rules Proc. AAAI '94 Workshop Knowledge Discovery in Databases, pp. 181192, July 1994.
[16] R.J. Miller and Y. Yang, “Association Rules Over Interval Data,” Proc. 1997 ACMSIGMOD Int'l Conf. Management of Data, pp. 452461, May 1997.
[17] R. Ng, L.V.S. Lakshmanan, J. Han, and A. Pang, “Exploratory Mining and Pruning Optimizations of Constrained Associations Rules,” Proc. 1998 ACMSIGMOD Int'l Conf. Management of Data, pp. 1324, June 1998.
[18] B. Ozden, S. Ramaswamy, and A. Silberschatz, Cyclic Association Rules Proc. 14th Int'l Conf. Data Eng., Apr. 1998.
[19] J. Park, M. Chen, and P. Yu, Efficient Parallel Data Mining for Association Rules Proc. Fourth Int'l Conf. Information and Knowledge Management, pp. 3136, 1995.
[20] J.S. Park, M.S. Chen, and P.S. Yu, “An Effective HashBased Algorithm for Mining Association Rules,” Proc. 1995 ACMSIGMOD Int'l Conf. Management of Data, pp. 175186, May 1995.
[21] J. Pearl, Graphs, Causality and Structural Equation Models Technical Report R253, Univ. of California, Los Angeles, 1998.
[22] K. Rajamani, B. Iyer, and A. Chadha, Using DB2's Object Relational Extensions for Mining Association Rules Technical Report TR 03,690, Santa Teresa Laboratory, IBM Corp., Sept. 1997.
[23] S. Ross, A First Course in Probability. fifth ed., PrenticeHall, 1998.
[24] R. Srikant and R. Agrawal, “Mining Generalized Association Rules,” Proc. 1995 Int'l Conf. Very Large Data Bases, pp. 407419, Sept. 1995.
[25] R. Srikant and R. Agrawal, “Mining Quantitative Association Rules in Large Relational Tables,” Proc. 1996 ACMSIGMOD Int'l Conf. Management of Data, pp. 112, June 1996.
[26] C. Silverstein, S. Brin, R. Motwani, and J. Ullman, “Scalable Techniques for Mining Causal Structures,” Proc. 1998 Int'l Conf. Very Large Data Bases, pp. 594605, Aug. 1998.
[27] P. Sprites, C. Glymour, and R. Scheines, Causation, Prediction and Search. New York: SpringerVerlag, 1993.
[28] T. Shintani and M. Kitsuregawa, Hash Based Parallel Algorithms for Mining Association Rules Proc. Conf. Parallel and Distributed Information Systems, pp. 1930, 1996.
[29] T. Shintani and M. Kitsuregawa, Parallel Mining Algorithms for Generalized Association Rules With Classification Proc. 1998 ACM SIGMOD Int'l Conf. Management of Data, pp. 2536, 1998.
[30] 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. 432443, Sept. 1995.
[31] S. Sarawagi, S. Thomas, and R. Agrawal, “Integrating Association Rule Mining with Databases: Alternatives and Implications,” ACM SIGMOD Int'l Conf. Management of Data, June 1998.
[32] R. Srikant, Q. Vu, and R. Agrawal, Mining Association Rules With Item Constraints Proc. Third Int'l Conf. Knowledge Discovery and Data Mining, 1997.
[33] H. Toivonen, “Sampling Large Databases for Association Rules,” Proc. 1996 Int'l Conf. Very Large Data Bases, pp. 134145, Sept. 1996.
[34] M.S. Viveros, J.P. Nearhoe, and M.J. Rothman, Applying Data Mining Techniques to a Health Insurance Information System Proc. 22nd Int'l Conf. Very Large Databases, Dec. 1996.
[35] Adult Data Set,http://www.cs.toronto.edu/~delve/data/adult desc.html, 1996.
[36] Test Results for the Real Data Set,http://www.comp.nus.edu.sg/~lizhao/research Appen dixA.doc, 2002.
[37] The Insurance Company Benchmark,http://kdd.ics.uci.edu/databases/tictic.html , 2000.
[38] CoIL Challenge 2000: The Insurance Company Case. P. van der Putten and M. van Someren, eds., published by Sentient Machine Research, Amsterdam. Also a Leiden Institute of Advanced Computer Science Technical Report 200009, June 2000.