Abstract

Mining Association Rules from huge databases is one of the important issue that need to be addressed. This paper presents a new sampling based association rule mining algorithm that uses a progressive sampling approach based on negative border and Frequent pattern growth (FP Growth) algorithm for finding the candidate item sets which ultimately shortens the execution time in generating the candidate itemsets. Experimental results reveals that the propsed approach is significantly more efficient than the Apriori based sampling approach.

References

• R.Agarwal and R.Srikant,”Fast algorithms for mining association rules”. In Proc. VLDB Conf., pp 487-499.
• R.Agrawal, T.Imielinski, and A.Swami, “Mining association rules between sets items in large databases”, in proceedings of the ACM SIGMOD Int'l Conf. on Management of data, pp. 207- 216, 1993.
• Basel A. Mahafzah, Amer F. Al-Badarneh and Mohammed Z. Zakaria "A new sampling technique for association rulemining," in Journal of Information Science, Vol. 35, pp. 358-376, 2009.
• S. Brin, R. Motwani, J. D. Ullman and S. Tsur, “Dynamic Itemset Counting and Implication Rules for Market Basket Data,” Proc. ACM SIGMOD, 1997, pp. 255-264.
• B. Chen, P.Haas, and P.Scheuermann,” A new two phase sampling based algorithm for discovering association rules”,SIGKDD, 2002.
• Cai-Yan Jia and Xie-Ping Gao, "Multi- scaling sampling: an adaptive sampling method for discovering Science and Technology archive, Vol. 20, pp. 309-318, 2005.
• Chuang K, Chen M, Yang .W,”Progressive Sampling for Association Rules based on Sampling Error Estimation”, Lecture notes in computer Science, Vol. 3518, pp. 505- 515,2005.
• J.Han, J.Pei, and Y.Yin,”Mining frequent patterns without candidate generation”, SIGMOD,2000.
• Hannu Toivonen, "Sampling Large Databases for Association Rules", Proceedings of the 22nd International Conference on Very Large Data Bases, pp: 134 - 145, 1996 SIGMOD,2000.
• Klaus Julisch," Data Mining for Intrusion Detection -A Critical Review" in proc. Of IBM Research on application of Data Mining in Computer security, Chapter 1 , 2002.
• J. S. Park, M. S. Chen, and P. S. Yu, “An Effective Hash based Algorithm for mining association rules,” Proc. ACM SIGMOD Conf Management of Data, May, 1995.
• Parthasarathy, S., "Efficient progressive sampling for association rules", IEEE International Conference on Data mining, pp: 354- 361, 2002.
• Raymond Chi-Wing Wong, Ada Wai- Chee Fu, "Association Rule Mining and its Application to MPIS", 2003.
• V.Umarani, M.Punithavalli,” On developing an effectual progressive sampling based approach for Association Rule Discovery”, In the proceedings of 2nd IEEE ICIME Int’l conference on Information and Data Management”, Chengdu,China.
• Venkatesan T. Chakaravarthy, Vinayaka Pandit and Yogish Sabharwal, "Analysis of sampling techniques for association rule mining," In Proceedings of the 12thInternational Conference on Database Theory, Vol. 361, pp. 276-283,2009.
• M. J. Zaki, S. Parthasarathy, W. Li, and M. Ogihara,“Evaluation of Sampling for Data Mining of Association Rules,” Technical Report 617, CS Dept., U. Rochester, May 1996.
• Y. Zhao, C. Zhang and S. Zhang, “Efficient frequent itemsets mining by sampling,” Proceedings of the fourth International Conference on Active Media Technology (AMT), pp. 112-117,