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A Fast Algorithm for Multilevel Association Rule Using Hash Based Method

Pratima Gautam, Dr. K. R. Pardasani


Data mining is having a vital role in many of the applications like market-basket analysis, in biotechnology field etc. In data mining, frequent itemsets plays an important role which is used to identify the correlations among the fields of database. The problem of developing models and algorithms for multilevel association mining pose for new challenges for mathematics and computer science. In most of the studies, multilevel rules will be mined through repeated mining from databases or mining the rules at each individually levels, it affects the efficiency, integrality and accuracy. This paper proposes a hash based method for multilevel association rule mining, which extracting knowledge implicit in transactions database with different support at each level. The proposed algorithm adopts a top-down progressively deepening approach to derive large itemsets. This approach incorporates boundaries instead of sharp boundary intervals. An example is also given to demonstrate that the proposed mining algorithm can derive the multiple-level association rules under different supports in a simple and effective manner.


Association Rule, Multilevel, Frequent Items, Transactional Database.

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Han, Y. Fu, “Mining Multiple-Level Association Rules in Large Databases,” IEEE TKDE. vol.1, 1999 pp. 798-805.

N.Rajkumar, M.R.Karthik, S.N.Sivana, S.N. Sivanandamndam,"Fast Algorithm for Mining Multilevel Association Rules," IEEE, Vol-2, 2003, pp . 688-692.

R.S Thakur, R.C. Jain, K.R.Pardasani, Fast Algorithm for Mining Multilevel Association Rule Mining," Journal of Computer Science, Vol-1, 2007, pp no: 76-81.

R. Agrawal, T. Imielinski, A. Swami, "Mining association rules between sets of items in large databases," In Proceeding ACM SIGMOD Conference, 1993, pp. 207-216,.

Scott Fortin,Ling Liu,"An object-oriented approach to multi-level association rule mining," Proceedings of the fifth international conference on Information and knowledge management, 1996, pp.65-72,.

R. Agrawal and R. Srikant. "Fast algorithms for mining association rules," In Proceedings of the 20th VLDB Conference, 1999, pp. 487-499.

Roberto Bayardo, “Efficiently mining long patterns from databases”, in ACM SIGMOD Conference 1998.

R. Agarwal, C. Aggarwal and V. Prasad, “A tree projection algorithm for generation of frequent itemsets,” Journal of Parallel and Distributed Computing, 2001.

K. Gouda and M.J.Zaki, “Efficiently Mining Maximal Frequent Itemsets”, in Proc. of the IEEE Int. Conference on Data Mining, San Jose, 2001.

R. Agrawal, T. Imielienski and A. Swami, “Mining association rules between sets of items in large databases,” In P. Bunemann and S. Jajodia, editors, Proceedings of the ACM SIGMOD Conference on Management of Data, 1993, Pages 207-216.

Ha Y, Cai, N Cercone,” Data-driven of quantitative rules in relational databases,” IEEE Tram Knowledge and data Engineering, vol. 5, 1993, pp. 29-40,

A.M.J. Md. Zubair Rahman, P. Balasubramanie and P. Venkata Krihsna, “A Hash based Mining Algorithm for Maximal Frequent Item Sets using Linear Probing,” International journal of computer science, vol.8, 2009, pp. 14-19,.

Burdick, D., M. Calimlim, J.Gehrke, “MAFIA: A maximal frequent itemset algorithm for transactional databases,” In International Conference on Data Engineering, 2001, pp. 443 – 452,.

Yin-Bo, Wan, Yong Liang, Li-Ya Ding, “Minig Multilevel Association Rules with Dynamic concept Hierarchy,” Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, Kunming, 2008, pp.287-292.

Predrag Stanišić, Savo Tomović, "Apriori Multiple Algorithlm for Mining Association Rules", 124X Information Techonology and Control, vol.37 2008, pp.311-320.

J. Han, M. Kamber, "Data Mining: Concepts and Techniques," The Morgan Kaufmann Series, 2001.

Park, J. S. Chen, M.S. Yu, P. S, “An Effective Hash Based Algorithm for Mining Association Rules,” In Proc. of the ACM-SIGMOD Conf. on Management of Data, Vol. 24, 1995, pp. 175-186.

Gunopulos, G, Mannila, H, Saluja, , “Discovering All Most Specific Sentences by Randomized Algorithms,” In Proc. of the 6th Int’l Conf. on Database Theory, Vol.1186, 1997, pp. 215-229.

Savasere, A. Omiecinski, E. Navathe, “ An Efficient Algorithm for Mining Association Rules in Large Databases,” In Proc. of the 21st Conf. on Very Large Data-Bases, 1995, pp. 432-444.

M. J. Zaki, “Scalable Algorithms for Association Mining,”. IEEE Transactions on Knowledge and Data Engineering, Vol. 12, No. 3, 2000, pp. 372-390.

H. Toivonen, “Sampling Large Databases for Association Rules,” VLDB Conference, 1996, pp. 134-145,.

S.J, Yen, A.L.P. Chen, “An Efficient Approach to Discovering Knowledge from Large Databases,” Fourth Int'l Conf. Parallel and Distributed Information Systems, 1996 pp. 8-18.

S. Brin, R. Motwani, J. Ullman, S. Tsur, “Dynamic Itemset Counting and Implication Rules for Market Basket Data,” ACM SIGMOD Conf. Management of Data, 1997.

R.J. Bayardo,”Efficiently Mining Long Patterns From Databases,” ACM SIGMOD Conf. Management of Data, 1998.

Virendra Kumar Shrivastava, Dr. Parveen Kumar and Dr. K. R. Pardasani, “FP-tree and COFI Based Approach for Mining of Multiple Level Association Rules in Large Databases,” (IJCSIS) International Journal of Computer Science and Information Security, Vol. 7 No. 2, 2010, pp.273-279.

Li Pingxaing Chen, Jianping and Bian Fuling, “A developed algorithm of Apriori based on association analysis,” published Geo-spatial Information Science (quarterly), vol-7, 2004 pp.104-112.

Yue XU, Gavin SHAW, Yuefeng LI,"Concise Representations for Association in Multilevel Datasets",Systems Engineering Society of China & Springer-Verlag, vol.18 (1), 2009, pp.53-70.

Jaiwei Han, Jian Pei, “mining frequent pattern by pattern growth methodology and implication,” SIGKDD Exploration, vol-2, 2000 pp no: 14-20.

An Chen, Hui Lin Ye,”multiple-level sequential pattern discovery from customer transaction databases,” international journal computational intelligence, Vol-1, 2003, pp.48-56,.

Ke Luo and Xue Maozhang. “An efficient frequent item set mining algorithm,”ACM-SIGMOD, Vol-2, 2007.pp.756-76.


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