Mining Low, Medium and High Profit Customers Over Transactional Data Stream

In Frequent Itemset Mining each item in transaction is represented by a binary value means 1 for present and 0 for absent. But There are several other parameter are also important like quantity, price or and profit of each item. Quantity, price or and profit these parameter are important in retail markets to find high utility itemset. High utility item set are those items which have utility value larger than a user specified value of minimum utility. The basic meaning of utility is the profitability of items to the users. However, quantity is significant for addressing real world decision problems that require maximizing the utility in an organization. For making business customer relationship management in retail markets customer base is the prime objective [17,20]. Data mining techniques are nowadays used to predict buying behavior of customers by analyzing transactional data. This paper introduces effective customer classification in retail marketing by using transactional utility value. This helps the business man to find those customers which contribute maximum profit to the overall transaction scenario