Abstract: Mining of frequent item sets is one of the most fundamental problems in data mining applications. My proposed algorithm which guides the seller to select the best attributes of a new product to be inserted in the database so that it stands out in the existing competitive products, due to budget constraints there is a limit, say m, on the number of attribute that can be selected for the entry into the database. Although the problems are NP complete. The Approximation algorithm are based on greedy heuristics. My proposed algorithm performs effectively and generates the frequent item sets faster.