E-business intelligence via MCMP-based data mining methods
WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
A novel method for extension transformation knowledge discovering
APWeb'12 Proceedings of the 14th international conference on Web Technologies and Applications
Credit card churn forecasting by logistic regression and decision tree
Expert Systems with Applications: An International Journal
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The email has profoundly affected our ways of life. In the year 2001, many websites begin operating the charge emails in china. Recently the foreign internet company serves free email with large storage capability, which threats the existence of charge email. The churn of email users is serious. This paper studied the churn of the customer using the way of data mining based on the background introduced. There are a lot models and study papers on data mining. This is the same on the churn. But there is few study paper about the churn of charge email based on data mining. So this is the innovation of this paper. Then we analyzed the data after processing the data by way of data cleaning and data integration based on the method of data mining. The data includes the logdata which contain the information of using the email and the database-data which was provided when the client applied the email. The model which I used to train the data is decision tree (see5). The models I get after the operation implicate the feature of the churn before they stop using the email. By reasonable explanation of the model, we can help the enterprise to improve the customer relationship management (CRM) and redeem the customers who would leave the email.