TY - JFULL AU - Damith Senanayake and Lakmal Muthugama and Laksheen Mendis and Tiroshan Madushanka PY - 2015/4/ TI - Customer Churn Prediction: A Cognitive Approach T2 - International Journal of Computer and Information Engineering SP - 766 EP - 773 VL - 9 SN - 1307-6892 UR - https://publications.waset.org/pdf/10000977 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 99, 2015 N2 - Customer churn prediction is one of the most useful areas of study in customer analytics. Due to the enormous amount of data available for such predictions, machine learning and data mining have been heavily used in this domain. There exist many machine learning algorithms directly applicable for the problem of customer churn prediction, and here, we attempt to experiment on a novel approach by using a cognitive learning based technique in an attempt to improve the results obtained by using a combination of supervised learning methods, with cognitive unsupervised learning methods. ER -