Initializing Genetic Programming using Fuzzy Clustering and its Application in Churn Prediction in the Telecom Industry

Bashar Awad Al-Shboul · Hossam Faris · Nazeeh Ghatasheh

12 citations0/0 files storedMalaysian Journal of Computer Science

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Customer churn and segmentation Data Mining and Machine Learning Applications Evolutionary Algorithms and Applications

Abstract

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.

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References

2005Applying data mining to telecom churn management 2009Customer churn prediction by hybrid neural networks 2008Database Marketing: Analyzing and Managing Customers 2002Genetic programming and rough sets: A hybrid approach to bankruptcy classification 2008Genetic algorithm based neural network approaches for predicting churn in cellular wireless network services 2009Churn models for prepaid customers in the cellular telecommunication industry using large data marts 2005Customer Churn Prediction Using Improved One-Class Support Vector Machine 2010An extended support vector machine forecasting framework for customer churn in e-commerce

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