How to Improve Efficiency of Association Rule Mining
Keywords:
Data mining, Association rule, Apriori algorithm.Abstract
In this paper, we provide the preliminaries of basic concepts about association rule mining and survey the list of existing association rule mining techniques. Of course, a single article cannot be a complete review of all the algorithms, yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions that have yet to be explored.
References
Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207-216.
Agrawal, R. and Srikant, R. 1994. Fast algorithms for mining association rules. In Proc. 20th Int. Conf. Very Large Data Bases, 487-499.
Agarwal, R., Aggarwal, C. and Prasad, V., A tree projection algorithm for generation of frequent item sets. In J. Parallel and Distributed Computing, 2000.
Ashrafi, M., Taniar, D., Smith, K., A New Approach of Eliminating Redundant Association Rules, Lecture Notes in Computer Science, Volume 3180, 2004, Pages 465–474.
Ashrafi, M., Taniar, D., Smith, K., Redundant Association Rules Reduction Techniques, Lecture Notes in Computer Science, Volume 3809, 2005, pp. 254–263.
Baralis, E., Psaila, G., Designing templates for mining association rules. Journal of Intelligent Information Systems, 9(1): 7-32, July 1997.
Brin, S., Motwani, R., Ullman, J. D., and Tsur, S. 1997. Dynamic itemset counting and implication rules for market basket data. In SIGMOD 1997, Proceedings ACM SIGMOD International Conference on Management of Data, May 13–15, 1997, 255-264.
Brin, S., Motwani, R. and Silverstein, C., “Beyond Market Baskets: Generalizing Association Rules to Correlations,” Proc. ACM SIGMOD Conf., pp. 265-276, May 1997.
Cheung, D., Han, J., Ng, V., Fu, A. and Fu, Y. (1996), A fast distributed algorithm for mining association rules, in Proc. of 1996 Int'l. Conf. on Parallel and Distributed Information Systems, Miami Beach, Florida, pp. 31–44.
Cheung, D., Xiao, Y., Effect of data skewness in parallel mining of association rules, Lecture Notes in Computer Science, Volume 1394, Aug 1998, Pages 48–60.
Chuang, K., Chen, M., Yang, W., Progressive Sampling for Association Rules Based on Sampling Error Estimation, Lecture Notes in Computer Science, Volume 3518, Jun 2005, Pages 505–515.
Cristofor, L., Simovici, D., Generating an informative cover for association rules. In Proc. of the IEEE International Conference on Data Mining, 2002.
Das, A., Ng, W.-K., and Woon, Y.-K. 2001. Rapid association rule mining. In Proceedings of the tenth international conference on Information and knowledge management. ACM Press, 474-481.
Tien Dung Do, Siu Cheung Hui, Alvis Fong, Mining Frequent Item sets with Category-Based Constraints, Lecture Notes in Computer Science, Volume 2843, 2003, pp. 76–86.
Han, J. and Pei, J. 2000. Mining frequent patterns by pattern-growth: methodology and implications. ACM SIGKDD Explorations Newsletter 2, 2, 14-20.
Hegland, M., Algorithms for Association Rules, Lecture Notes in Computer Science, Volume 2600, Jan 2003, Pages 226–234.
Hilderman, R. J., Hamilton, H. J., Knowledge Discovery and Interest Measures, Kluwer Academic, Boston, 2002.
Jaroszewicz, S., Simovici, D., Pruning Redundant Association Rules Using Maximum Entropy Principle, Lecture Notes in Computer Science, Volume 2336, Jan 2002, pp. 135-142.
Li, Y., Gopalan, R., Effective Sampling for Mining Association Rules, Lecture Notes in Computer Science, Volume 3339, Jan 2004, Pages 391–401.
Liu, B., Hsu, W., Ma, Y., “Mining Association Rules with Multiple Minimum Supports,” Proc. Knowledge Discovery and Data Mining Conf., pp. 337-341, Aug. 1999.
Manning, A., Keane, J., Data Allocation Algorithm for Parallel Association Rule Discovery, Lecture Notes in Computer Science, Volume 2035, Page 413-420.
Omiecinski, E. (2003), Alternative Interest Measures for Mining Associations in Databases, IEEE Transactions on Knowledge and Data Engineering, Vol. 15, No. 1, pp. 57-69.
Parthasarathy, S., Zaki, M. J., Ogihara, M., Parallel data mining for association rules on shared-memory systems. Knowledge and Information Systems: An International Journal, 3(1): 1–29, February 2001.
Parthasarathy, S., Efficient Progressive Sampling for Association Rules. ICDM 2002: 354-361.
Ramaswamy, S., Mahajan, S., Silbershatz, A., “On the Discovery of Interesting Patterns in Association Rules,” Proc. Very Large Databases Conf., pp. 368-379, Sept. 1998.
Sarawagi, S., Thomas, S., “Mining Generalized Association Rules and Sequential Patterns Using SQL Queries”. In Proc. of KDD Conference, 1998.
Savasere, A., Omiecinski, E., Navathe, S.: Mining for strong negative associations in a large database of customer transactions. In: Proc. of ICDE. (1998) 494–502.
Schuster, A. and Wolff, R. (2001), Communication-efficient distributed mining of association rules, in Proc. of the 2001 ACM SIGMOD Int'l. Conference on Management of Data, Santa Barbara, California, pp. 473-484.
Sharma, L.K., Vyas, O.P., Tiwary, U.S., Vyas, R., A Novel Approach of Multilevel Positive and Negative Association Rule Mining for Spatial Databases, Lecture Notes in Computer Science, Volume 3587, Jul 2005, Pages 620–629.
Techapichetvanich, K., Datta, A., Visual Mining of Market Basket Association Rules, Lecture Notes in Computer Science, Volume 3046, Jan 2004, Pages 479–488.
Teng, W., Hsieh, M., Chen, M.: On the mining of substitution rules for statistically dependent items. In: Proc. of ICDM. (2002) 442–449.
Toivonen, H. (1996), Sampling large databases for association rules, in The VLDB Journal, pp. 134-145.
Tseng, M., Lin, W., Jeng, R., Maintenance of Generalized Association Rules Under Transaction Update and Taxonomy Evolution, Lecture Notes in Computer Science, Volume 3589, Sep 2005, Pages 336–345.
Verma, K., Vyas, O.P., Vyas, R., Temporal Approach to Association Rule Mining Using T-Tree and P-Tree, Lecture Notes in Computer Science, Volume 3587, Jul 2005, Pages 651–659.
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