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.