Rule-base data mining systems for customer queries

The main objective of this paper is to have a best association between customer and organisation. This method is proposed in order to discover knowledge from huge amount of data and to use the data efficiently because of great demand. Banking is the most commonly used application for financial section. In which, Enterprise Resource Planning (ERP) model is most widely used in order to cost control, accounting and e-business & analyses. The request of the customers are routed automatically to the next department when one department finishes their work of the customer´s request and each department have access to the single database that holds the customers new request. Customer Relationship Management (CRM) model is responsible for receiving the request and sending responses to the customers quickly and directly. The request includes queries, complaints, suggestions, and orders. These requests are forwarded to inner view ERP through query generator. In this paper, we proposed a model that integrates the customer queries, transactions, databases and all other specifications used in ERP Systems, then use data mining techniques to integrate decision making and forecasting. Using ERP characteristics, data gathered from central database are in cluster format which is based on action taken against the queries generated by customers. Later the clustered data´s are used by Apriori algorithm to extract new rules and patterns for the enhancement of an organisation.