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Published in:
2010 | OriginalPaper | Chapter
Using Genetic K-Means Algorithm for PCA Regression Data in Customer Churn Prediction
Authors : Bingquan Huang, T. Satoh, Y. Huang, M. -T. Kechadi, B. Buckley
Published in: Advanced Data Mining and Applications
Publisher: Springer Berlin Heidelberg
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Imbalance distribution of samples between churners and non-churners can hugely affect churn prediction results in telecommunication services field. One method to solve this is over-sampling approach by PCA regression. However, PCA regression may not generate good churn samples if a dataset is nonlinear discriminant. We employed Genetic K-means Algorithm to cluster a dataset to find locally optimum small dataset to overcome the problem. The experiments were carried out on a real-world telecommunication dataset and assessed on a churn prediction task. The experiments showed that Genetic K-means Algorithm can improve prediction results for PCA regression and performed as good as SMOTE.