Structure identification of fuzzy model
Fuzzy Sets and Systems
The Alternating Decision Tree Learning Algorithm
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A Churn-Strategy Alignment Model for Managers in Mobile Telecom
CNSR '05 Proceedings of the 3rd Annual Communication Networks and Services Research Conference
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Computer assisted customer churn management: State-of-the-art and future trends
Computers and Operations Research
GSM Churn Management Using an Adaptive Neuro-Fuzzy Inference System
IPC '07 Proceedings of the The 2007 International Conference on Intelligent Pervasive Computing
IEEE Transactions on Neural Networks
Self-learning fuzzy controllers based on temporal backpropagation
IEEE Transactions on Neural Networks
International Journal of Information Retrieval Research
Hi-index | 12.05 |
Churn management is important and critical issue for Global Services of Mobile Communications (GSM) operators to develop strategies and tactics to prevent its subscribers to pass other GSM operators. First phase of churn management starts with profile creation for the subscribers. Profiling process evaluates call detail data, financial information, calls to customer service, contract details, market details and geographic and population data of a given state. In this study, input features are clustered by x-means and fuzzy c-means clustering algorithms to put the subscribers into different discrete classes. Adaptive Neuro Fuzzy Inference System (ANFIS) is executed to develop a sensitive prediction model for churn management by using these classes. First prediction step starts with parallel Neuro fuzzy classifiers. After then, FIS takes Neuro fuzzy classifiers' outputs as input to make a decision about churners' activities.