Learning classification rules for telecom customer call data under concept drift
The application of the CD3 decision tree induction algorithm to telecommunications customer call data to obtain classification rules is described. CD3 is robust against drift in the underlying rules over time (concept drift): it both detects drift and protects the induction process from its effects. Specifically, the task is to data mine customer details and call records to determine whether the profile of customers registering for a 'friends and family' service is changing over time and to maintain a rule set profiling such customers. CD3 and the rationale behind it are described and experimental results on customer data are presented.
concept drift decision trees adaptive learning user profiling