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Discovering frequent itemset is a key difficulty in significant data miningapplications, such as the discovery of association rules, strong rules,episodes, and minimal keys. The problem of developing models and algorithms formultilevel association mining poses for new challenges for mathematics andcomputer science. In this paper, we present a model of mining multilevelassociation rules which satisfies the different minimum support at each level,we have employed princer search concepts, multilevel taxonomy and differentminimum supports to find multilevel association rules in a given transactiondata set. This search is used only for maintaining and updating a new datastructure. It is used to prune early candidates that would normally encounterin the top-down search. A main characteristic of the algorithms is that it doesnot require explicit examination of every frequent itemsets, an example is alsogiven to demonstrate and support that the proposed mining algorithm can derivethe multiple-level association rules under different supports in a simple andeffective manner

An Efficient Algorithm for Mining Multilevel Association Rule Based on Pincer Search