Abstract: The problem of discovering association rules has received considerable research attention and several fast algorithms for mining association rules have been developed. In practice, users are often interested in a subset of association rules . For example, they may only want rules that contain some specific items. Applyi ng such constraints as a pre-processing stepor integrating them into the mining algorithm can dramatically reduce the execution time. The problem of integrating constraints, that are Boolean expressions over the presence or absence of items , into the maximum frequent itemsets discovery algorithm is considered. An integ rated algorithm and its updating algorithm for mining maximum frequent itemsets with item constraints are presented and their tradeoff is discussed. which is ba sed on a novel frequent pattern tree (FP-tree) structure that is an extended pre fix-tree structure for storing compressed and crucial information about frequent patterns.