Research on the Model of Missing Information Completion of Telecom Customers Based on Factor Analysis and Data Mining

Abstract

The key problem that must be solved in the analysis and prediction of customer churn in telecom companies is the data completion of customer missing. In this paper, a model based on factor analysis and data mining is proposed to complete customer missing data. This model first completes the factors generated by the missing data, and then completes the missing data. In factor completion, the improved k-mean algorithm is used to effectively solve the problem of initial value and K value selection, and the Euclidean distance is improved to achieve effective clustering of factors and factor completion. The missing data value is obtained by factor reverse reasoning. The model is trained with real historical data and tested to verify that the model is effective.