A churn consumer can be defined as a customer who transfers from one service provider to another\r\nservice provider. Recently, business operators have investigated many techniques that identify the\r\ncustomer churn since churn rates leads to serious business loss. In this paper, a hybrid technique has\r\nbeen used which combines K-means clustering with Genetic Programming to predict churners in\r\ntelecommunication companies. First, K-means clustering is used to filter the training dataset from\r\noutliers and non representative customer behaviors then Genetic Programming is applied in order to\r\nbuild classification trees that are able to classify customers into churners and non churners. The\r\nproposed approach is evaluated and compared with other common classification approaches.\r\nExperimental results show that K-means clustering with Genetic Programming has promising\r\ncapabilities in predicting churners� rates.
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