A Case Study of Predicting Banking Customers Behaviour by Using Data Mining Data Mining (DM) is a technique that examines information stored in large database or data warehouse and find the patterns or trends in the data that are not yet known or suspected. DM techniques have been applied to a variety of different domains including Customer Relationship Management CRM). In this research, a new Customer Knowledge Management (CKM) framework based on data mining is proposed. The proposed data mining framework in this study manages relationships between banking organizations and their customers. Two typical data mining techniques  Neural Network and Association Rules  are applied to predict the behavior of customers and to increase the decisionmaking processes for recalling valued customers in banking industries. The experiments on the real world dataset are conducted and the different metrics are used to evaluate the performances of the two data mining models. The results indicate that the Neural Network model achieves better accuracy but takes longer time to train the model.

Uncertain decision tree for bank marketing classification Abstract   This study proposes a novel decision tree for uncertain data, called the uncertain decision tree (UDT), based on the uncertain genetic clustering algorithm (UGCA). UDT extends the decision tree to handle data with uncertain information, in which the uncertainty must be considered to obtain high quality results. In UDT, UGCA automatically searches for the proper number of branches of each node, based on the classification error rate and the classification time of UDT. Restated, UGCA reduces both the classification error rate and computing time and, then, optimizes the proposed UDT. Before the UDT is designed using UGCA, an uncertain merging algorithm (UMA) is also developed to reduce the uncertain data set, thereby allowing UGCA to process a large data set efficiently. Importantly, experimental results demonstrate that the proposed UDT outperforms traditional uncertain decision trees.