Data Mining Technique to Predict Missing Items and Find Optimal Customer for Beneficial Customer Relationship and Management

Abstract: The aim of association rule mining is to find frequently co-occurring groups of items in transactional databases. The intention of this knowledge is for prediction purposes. This paper contributes a technique that uses the partial information about the contents of a shopping cart for the prediction of products that the customers wish to buy or are more likely to buy along with the already bought products. So this paper presents a technique called the "Combo Matrix" whose principal diagonal elements shows the association between items and looking to the principal diagonal elements, the customer can choose different items that can be bought with the purchased contents of the shopping cart and also reduces the rule mining cost. In this paper, we also propose a data mining and artificial technique to maintain the customer relationship between company and customers. For this purpose, we maintain a historical database and then we use data mining ARM technique to get the customer information from this database. Also we use Customer Relationship Management (CRM) systems which are developed and used to support marketing, customer interactions, preferences and data.

Keywords: Association rule mining, Prediction, Combo Matrix, Customer Relationship Management, data mining.