Abstract
Customer churn has emerged as a critical issue for Customer Relationship Management and customer retention in the telecommunications industry, thus churn prediction is necessary and valuable to retain the customers and reduce the losses. Recently rule-based classification methods designed transparently interpreting the classification results are preferable in customer churn prediction. However most of rule-based learning algorithms designed with the assumption of well-balanced datasets, may provide unacceptable prediction results. This paper introduces a Fuzzy Association Rule-based Classification Learning Algorithm for customer churn prediction. The proposed algorithm adapts CAIM discretization algorithm to obtain fuzzy partitions, then searches a set of rules using an assessment method. The experiments were carried out to validate the proposed approach using the customer services dataset of Telecom. The experimental results show that the proposed approach can achieve acceptable prediction accuracy and efficient for churn prediction.
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Acknowledgement
The authors gratefully acknowledge the research support from the EU Framework Programme 7, Marie Curie Actions under grant No. PIRSES-GA-2009-247608.
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Huang, B., Huang, Y., Chen, C., Kechadi, M.T. (2016). A Fuzzy Rule-Based Learning Algorithm for Customer Churn Prediction. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2016. Lecture Notes in Computer Science(), vol 9728. Springer, Cham. https://doi.org/10.1007/978-3-319-41561-1_14
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DOI: https://doi.org/10.1007/978-3-319-41561-1_14
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