Effective Evaluation of Transactional Data Items – A KDD Approach toward the customer rating

Abstract: In the Market Basket Analysis Frequent pattern mining plays the vital role in finding occurrence frequency of the items in a transactional dataset. By this frequent pattern mining analysis every retail company are aimed at maximizing the social relationship with the customer. If we use only frequent pattern mining then it generate a large amount of itemset which is very difficult to perform the in-memory analysis. So we have proposed a new approach of finding the item frequency by associating a weight or utility factor based upon the usage, demand etc. of the item. Finally the clustering techniques have been used to classify the customer from the final transactional dataset which will help to strengthen the social relationship between the retail organization and their customer.

Keywords: CRM, Min-Sup, Cluster of the customers, data mining.