A Novel Approach for Providing the Customer Churn Prediction Model using Enhanced Boosted Trees Technique in Cloud Computing
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10.5120/19987-6449 |
Kiranjot Kaur and Sheveta Vashisht. Article: A Novel Approach for Providing the Customer Churn Prediction Model using Enhanced Boosted Trees Technique in Cloud Computing. International Journal of Computer Applications 114(7):1-7, March 2015. Full text available. BibTeX
@article{key:article,
author = {Kiranjot Kaur and Sheveta Vashisht},
title = {Article: A Novel Approach for Providing the Customer Churn Prediction Model using Enhanced Boosted Trees Technique in Cloud Computing},
journal = {International Journal of Computer Applications},
year = {2015},
volume = {114},
number = {7},
pages = {1-7},
month = {March},
note = {Full text available}
}
Abstract
Organizations earns huge amount of money by providing the different services to their customers. In today's world of competition, organizations need to focus on customer relationship management. Retaining the existing customers is as much important as attracting the new customers for an organization. For this purpose, organizations use data mining techniques for segmenting the churn customers and loyal customers so that special offers can be provided to churn customers to retain them as customers are the most valuable asset for organizations. The aim of this paper is to provide a customer churn prediction model using a standard CRISP-DM methodology based on RFM and Boosted Trees Technique. To enhance the performance of the technique, hybrid approach for building classifiers is used. There is also a comparison between the performances of both techniques. Results show that enhanced boosted trees technique performs better than existing boosted tree technique. Proposed approach is then implemented on the cloud environment to provide the cloud facilities for mining the data.
References
- Wang, C. and Wang, Y. 2012. Discovering Consumer's Behavior Changes Based on Purchase Sequences. In Proceedings of the 9th IEEE-International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2012), 642-645
- Berry, M. J. and Linoff, G. 1997. Data Mining Techniques: For Marketing, Sales, and Customer Support, John Wiley & Sons, New York, NY, USA.
- Han, J. and Kamber, M. 2006. Data Mining: Concepts and Techniques, Morgan Kaufmann, India
- http://publib. boulder. ibm. com/infocenter/db2luw/v9r5/index. jsp?topic=%2Fcom. ibm. im. easy. doc%2Fc_dm_process. html
- Buyya, R. , Yeo, C. S. , Venugopal, S. , Broberg, J. , and Brandic, I. 2009. Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility, Future Generation Computer Systems, 25, 599-616.
- Lingjuan, L. and Zhang, M. 2011. The Strategy of Mining Association Rule Based on Cloud Computing. In Proceedings of the IEEE-International Conference on Business Computing and Global Informatization, 475 – 478
- Hadden, J. , Tiwari, A. , Roy, R. , and Ruta, D. 2007. "Computer assisted customer churn management: State-of-the-art and future trends", Computers and Operations Research, Vol. 34, Issue 10, 2902-2917.
- http://en. wikipedia. org/wiki/AdaBoost
- Kaur, K. and Vashisht, S. "Enhanced Boosted Tree Technique for Customer Churn Prediction Model", IOSR Journal of Engineering (IOSRJEN), 2014, Vol. 4, Issue 3, 41-45
- Basiri, J. , Taghiyareh, F. , and Moshiri, B. 2010. A Hybrid Approach to Predict Churn. In Proceedings of IEEE Asia-Pacific Services Computing Conference, Hangzhou, China, 485-491.
- Nabavi, S. and Jafari, S. 2013. Providing a Customer Churn Prediction Model using Random Forest Technique. In proceedings of 5th IEEE-Conference on Information and Knowledge Technology (IKT), 202-207
- Buckinx, W. and Van Den Poel, D. "Customer Base Analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting". European Journal of Operational Research, 2005, Vol. 164, 252-268.
- http://en. wikipedia. org/wiki/Decision_stump
- http://nltk. googlecode. com/svn/trunk/doc/book/ch06. html
- http://en. wikipedia. org/wiki/AODE