一种基于改进RFM模型的数字集群用户分类方法

数字集群系统具有组呼和半双工通信等特点，针对传统用户分类方法不能满足数字集群用户分类需求的问题，提出一种基于改进RFM模型的数字集群用户分类方法。首先引入平均讲话时长属性建立RVS（recency vitality speak）模型；然后采用层次分析法确定RVS模型参数的权重；最后，利用K-means++聚类算法对数字集群用户进行分类。仿真结果表明，使用提出的用户分类方法，数字集群用户分类的准确度可达到87.9%以上。

Digital cluster user classification method based on improved RFM model

The digital trunking system has the characteristics that the calling mode is mainly group calling and the communication mode is mostly half duplex. For the problem that the traditional user classification method couldn''t meet the classification requirements of digital cluster users, this paper proposed a digital cluster user classification method based on improved RFM model. Firstly, it introduced the average speech duration attribute to establish the RVS(recency vitality speak) model. Then, it used the analytic hierarchy process to determine the weight of each parameter in the model. Finally, it used the K-means++ clustering algorithm to classify digital cluster users. The simulation result shows that, by using the user classification method proposed in this paper, the accuracy of digital cluster user classification can reach more than 87.9%.