Abstract: Mining of frequent item sets is one of the most fundamental problems in data mining applications. A typical example is Market Basket analysis. In this method or approach it examines the buying habits of the customers by identifying the frequent items purchased by the customers in their baskets. This helps to increase in the sales of a particular product. This paper mainly focuses on the study of the existing data mining algorithm for Market Basket data. 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.
Keywords: Association Rule Mining, Market Basket Analysis, Mining frequent item sets, intersection pruning, and data-set condensing.