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Hybrid Models Using Unsupervised Clustering for Prediction of Customer Churn
2009
Journal of Organizational Computing and Electronic Commerce
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In this paper, we use two-stage hybrid models consisting of unsupervised clustering techniques and decision trees with boosting on two different data sets and evaluate the models in terms of top decile lift. We examine two different approaches for hybridization of the models for utilizing the results of clustering based on various attributes related to service usage and revenue contribution of customers. The results indicate that the use of clustering led to improved top decile lift for the
doi:10.1080/10919390902821291
fatcat:tgmaop5v4vhk7f6n2m4jbbztz4