Abstract: Facing with the high dimensional,nonlinear and unbalanced data problems of churn prediction of E-business customers,it is difficult to improve the accuracy of churn prediction of E-business customers by applying traditional methods.Hence an integration model for churn prediction of E-business customers based on objective system analysis (OSA) and group method of data handling (GMDH),two important selforganized data mining (SODM) algorithms,is presented in this paper.Firstly,the key attributes are automatically selected using OSA algorithm.Then GMDH network is trained with training samples,which is used to identify customer churn status of testing samples.Up-sampling metod is also used in this paper to balance the churn-customer data and unchurn-customer data to improve the forecasting accuracy.This proposed approach is applied for chum prediction of an online shop,which proves that compared with some common approaches such as artificial neural networks and support vector machines,more accuracy forecasted results can be obtained.

Key words: self-organized data mining, objectire system analysis, group method of data mining, churn pre-diction, E-business