User behavior pattern mining method based on multi-dimension and multi-granularity analysis in telecom networks

Abstract

In order to better understand the behavior of users in telecom networks,it takes CDR (call detail record) data of large-scale telecom network as the research object.By using the mixed probability model and feature engineering method,the multi-dimension characteristics of the call time,call frequency and connections are analyzed from the perspective of user groups and individuals.It is further refined from different time granularities such as hour,day,and week to realize effective discovery of call behavior patterns for different user groups.The distribution characteristics of user behavior are modeled by mixed probability model,which solves the problem of describing the distribution characteristics such as user's call time and frequency.Based on the dataset of a regional telecom network,the performance of decision tree,naive Bayes and SVM classification algorithm are compared.It proves the validity and computational feasibility of the proposed method.The differences in communication behavior patterns of different groups are also compared by taking the service numbers like express,flight and bank as examples.

Keywords

• telecom network
• multi-dimension
• multi-granularity
• mixture-of-gaussian
• behavior pattern mining