Prediction in shopping cart uses partial information about the contents of a shopping cart for the prediction of what else the customer is likely to buy. In order to reduce the rule mining cost, a fast algorithm generating frequent item sets without generating candidate item sets is proposed. The algorithm uses Boolean vector with relational AND operation to discover frequent item sets and generate the association rule. Association rules are used to identify relationships among a set of items in database.