چكيده به لاتين
The movement towards cashless society has transformed payment industry. Rapid evolution and presentation of alternative strategies by new competitors have left traditional activists facing serious threats. Welcoming innovation and having the ability to respond rapidly in this industry is crucial to survival. The winner of such a competition is the one which takes practical measures in particular fields such as replacing cash, performing activities in e-commerce, and using advantages of big data analysis. This study has two purposes. The first purpose is to investigate factors affecting on acclaiming various e-banking service. Therefore, information and data from central bank of Islamic republic of Iran about successful transaction using different e-banking tools such as ATM, terminal branches, point of sale, mobile banking and internet banking were analyzed. In addition, the functions of banks in country were compared and analyzed according to their e-banking service. In order to discover the tactic knowledge in existing information and data, some data mining techniques such as clustering and decision tree were used. By using these techniques the efficiency of factors such as penetration coefficient of internet, number of mobile users, unemployment rate and IT development index of each province on successful transactions are investigated through e-payment tools of each province. In this study, an algorithm based on the K-Means clustering algorithm is presented. Two main purposes are defined for the proposed algorithm. Helping cluster labeling in clustering and selecting the influential features of the dataset are the goals and applications of the proposed algorithm.
