Electronic banking plays a central role in the field of electronic payment. The aim of this study is to analyze the behavior of Tejarat Bank e-banking customers using data mining based on RFM model. This study has analyzed and examined e-banking customers in 8 clusters through the analysis of 1700 data and determining the optimal number of clusters by two-step algorithm and determining 8 optimal clusters by the algorithm and using k-mean algorithm. The results show that the cluster which has lower R (exchange recency), lower F (the number of exchange frequency) and higher M (Monetary value) has more loyal customers. The cluster which has lower R is placed in the first cluster rank. In spite of RFM variables, gender, age and education has been found to be effective on clustering. Results of clustering contains 8 clusters: 1) men with the age range of 30 to 40 years old with Bachelor's degree; 2) men with the age range of 30 to 40 years old with a bachelor's degree; 3) women under 30 years old with high school diploma; 4) men with the age range of 40 to 50 years old with high school diploma; 5) men with the age range of 40 to 50 years old with bachelor's degree; 6) men over 50 years old with high school diploma; 7) women under the age of 30 years old with higher education; 8) men under 30 years old with Bachelor's degree.
