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Customer defection or "churn" rate is critically important since it leads to serious business loss. Therefore, many telecommunication companies and operators have increased their concern about churn management and investigated statistical and data mining based approaches which can help in identifying customer churn. In this paper, a churn prediction framework is proposed aiming at enhancing the predictability of churning customers. The framework is based on combining two heuristic approaches; Fast Fuzzy C-Means (FFCM) and Genetic Programming (GP). Considering the fact that GP suffers three different major problems: sensitivity towards outliers, variable results on various runs, and resource expensive training process, FFCM was first used to cluster the data set and exclude outliers, representing abnormal customers’ behaviors, to reduce the GP possible sensitivity towards outliers and training resources. After that, GP is applied to develop a classification tree. For the purpose of this work, a data set was provided by a major Jordanian telecommunication mobile operator.