Applied research on customer´s consumption behavior of bank POS machine based on data mining

According to the data feature of customer´s consumption records of bank POS machine and the analysis depending on the actual requirements, a new modeling framework on consumption behavior of bank POS machines is presented in this paper, and further research on the implementation method of main aspects in the model is carried out. Firstly, we conduct data discretization and customer segmentation by K-means algorithm and Kohonen network clustering algorithm respectively, analyze and compare the results comprehensively, and ultimately get the optimum result of extracting high quality customer. Then in the process of mining the high quality customer´s consumption characteristic, we use C5.0 algorithm to analyze the experiment and evaluate the experimental results after getting the high quality customer´s consumption records through customer segmentation results. Finally we get the knowledge base of high quality consumer´s consumption behavior which can give support to banks and merchants to make a favorable decision.