Electronic Commerce Based on Self-Organizing Data Mining Customer Churn Prediction Model

Abstract

In order to solve the high dimensional and nonlinear problems of churn prediction of E-business customers, this paper proposes a novel model for churn prediction of E-business customer based on Self-Organized Data Mining (SODM). In this model, Objective System Analysis (OSA) and improved Group Method of Data Handling (GMDH) , two important SODM algorithms, are integrated for the churn prediction of E-business customer. At first, the critical attributes of E-business customer chum are chosen with OSA and then the training samples are sent to improved GMDH for studying and training anthe status of customer chum of testing sample is identified. The approach has been applied to the empirical analysis on the prediction of E-customer chum, which proves that compared with some common approaches, this integrated model based on SODM is an efficient and practical tool for the prediction of business chum and provides E-business enterprises with a new forecasting tool in customer relationship management.