关键词: 流失预测, 复杂网络, 数据挖掘, 遗传演化, 云计算

Abstract: Data mining based customer churn prediction is significant for telecom operator to promote customer service quality in keen competition among operators. A new churn prediction algorithm based on genetic evolution with less running time and better Lift value is proposed to promote predicting accuracy and adaptive ability of prediction model. The new algorithm GASPA (genetic algorithm based SPA) uses genetic evolution from artificial in-telligence to optimize parameters of psychology model SPA (spreading activation). GASPA algorithm is proved to reach better accuracy value than fixed-step method and better churn prediction Lift value than SPA on real mobile dataset. Aiming to handle very large scale dataset, the paralleled GASPA on MapReduce (M-GASPA) is proposed to enlarge data scale which can be processed. According to experiment results of iteration time, M-GASPA is a much faster algorithm than SPA while Lift value is promoted.

Key words: churn prediction, complex network, data mining, genetic evolution, cloud computing