The problems of developing models and algorithms for multilevel association mining pose for new challenges for mathematics and computer science. These problems become more challenging when some form of uncertainty in data or relationships in data exists. In this , we present a partition technique for the multilevel association rule mining problem. Taking out association rules at multiple levels helps in discovering more specific and applicable knowledge. In multilevel association rule there are two methods Boolean matrix and Hash based method. A Boolean Matrix based approach has been employed to discover frequent itemsets, the item forming a rule come from different levels. It adopts Boolean relational calculus to discover maximum frequent itemsets at lower level. When using this algorithm first time, it scans the database once and will generate the association rules. Apriori property is used in prune the item sets. It is not necessary to scan the database again; it uses Boolean logical operation to generate the multilevel association rules and also use top-down progressive deepening method. Hash-based algorithm for the candidate set generation. Explicitly, the number of candidate 2-itemsets generated by the proposed algorithm is, in orders of magnitude, smaller than that by previous methods, thus resolving the performance bottleneck. Note that the generation of smaller candidate sets enables us to effectively trim the transaction database size at a much earlier stage of the iterations, thereby reducing the computational cost for later iterations significantly. Extensive simulation study is conducted to evaluate performance of the proposed algorithm.
Association rules, Boolean matrix,data mining ,Hash based method, itemsets, multilevel rules
 R. Agrawal, T. Imielinski, and A. Swami, “Mining association rules between sets of items in large databases,”Proceedings of the ACM SIGMOD Conference on Management of data, pp. 207-216, 1993.
R. Agrawal and R. Srikant,” Fast algorithms for mining association rules,” In proceeding of the VLDBConference,1994.
 H. Mannila, H. Toivonen, and A, Verkamo. “Efficient algorithm for discovering association rules,” AAA1 Workshop on Knowledge Discovery in Databases.
 Hunbing Liu and Baishenwang, “An association Rule Mining Algorithm Based On a Boolean Matrix,” DataScience Journal, Vol-6, Supplement 9, S559-563, September 2007.
 R.S Thakur, R.C. Jain, K.R.Pardasani, "Fast Algorithm for Mining Multilevel Association Rule Mining," Journal ofComputer Science, Vol-1, pp. 76-81, 2007 .
 Ha and Y. Fu, “Mining Multiple-Level Association Rulesin Large Databases,” IEEE TKDE. Vol-1, pp. 798-805, 1999 .
 R. Agrawal, C. Faloutsos, and A. Swami. EfficientSimilarity Search in Sequence Databases, Proceedingsof the 4th Intl, conf, on Foundations of DataOrganization and Algorithms, October, 1993.
 R. Agrawal, S. Ghosh, T. Imielinski, B. IYer, andA. Swami. An Interval Classifier for Database MiningApplications. Proceedings of the 18th InternationalConference on Very Large Data Bases, pages560-573, August 1992.
 AnjnaPandey and K. R. Pardasani,”Rough Set Model forDiscovering Hybrid Dimensional Association Rules,”International Journal of Computer Science and NetworkSecurity, Vol -9, no.6, pp.159-164,2009