Efficient Attribute Selection to stand out in the market

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 NPcomplete.The Approximation algorithm are based on greedy heuristics. DCIP algorithm uses data-set condensing and intersection pruning to find the maximal frequent item set. The condensing process is performed by deleting items in infrequent 1-itemset and merging duplicate transactions repeatedly; the pruning process is performed by generating intersections of transactions and deleting unneeded subsets recursively. This algorithm differs from all classical maximal frequent item set discovering algorithms; experiments show that this algorithm is valid with moderate efficiency; it is also easy to code for use in KDD applications