The overall system execution flow is shown in Fig. 1, and it includes two parts: (1) the domain-driven mining phase and (2) the model tuning phase. In the first part, ETL (extraction transformation loading) is utilized to derive CDR historical data.
Next, this study uses association rules to analyze data about user bills based on telecom providers’ practices to create a behavioral model of potential late-paying users.
According to the derived rules, in combination with the professional knowledge of the providers, the
derived attributes from payment behavior is established.
Meanwhile, the clustering technique is then used to derive the desired groups with business interestingness.
Finally, decision tree algorithms are utilized to analyze the data by using various attributes,and the derived rules are stored in a database for validation