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Mitigating the Risk of Customer Churn Using K-Means Clustering

P. Sharmila, J. Ilakkiya

Abstract


The probability that a customer is not benefitted by investing in a particular share, while there is still a chance for calculating accurate result that changes in milliseconds, has a huge impact on the profit for the customer as well as the organization they is associated with. Considering this criteria, a new clustering algorithm called the K-Means clustering method (KMC) is proposed. There are speculative results that witness K-means has stronger clustering semantic strength than other clustering methods in data mining. Can also get suggestions to avoid the risk of investing in an unprofitable share.


Keywords


Churn, K-Means Clustering Method, Map- Reduce, Subtractive Clustering Method, Fuzzy C-Means, Semantic-Driven Subtractive Clustering Method, Axiomatic Fuzzy Sets.

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References


J. Hadden, A. Tiwari, R. Roy, and D. Ruta, “Computer assisted customerchurn management: State-of-the-art and future trends,” Comput. Oper. Res., vol. 34, no. 10, pp. 2902–2917, Oct. 2007.

N. Lu, H. Lin, J. Lu, and G. Zhang, “A customer churn prediction model in telecom industry using boosting,” IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1659–1665, May 2014.

B. Q. Huang, T. K. Mohand, and B. Brian, “Customer churn prediction in telecommunications,” Expert Syst. Appl., vol. 39, no. 1, pp. 1414–1425, Jan. 2012.

E. G. Castro and M.S.G. Tsuzuki, “Churn prediction in online games using players’ login records: A frequency analysis approach,” IEEE Trans. Comput. Intell. AI Games, vol. 7, no. 3, pp. 255–265, Sep. 2015.

W. H. Au, K. C. C. Chan, and Y. Xin, “A novel evolutionary data mining algorithm with applications to churn prediction,” IEEE Trans. Evol. Comput., vol. 7, no. 6, pp. 532–545, Dec. 2003.

S. Y. Hung, D. C. Yen, and H. Y.Wang, “Applying data mining to telecom churn management,” Expert Syst. Appl., vol. 31, no. 3, pp. 515–524, Oct. 2006.

T. Verbraken, V. Wouter, and B. Bart, “A novel profit maximizing metric for measuring classification performance of customer churn prediction models,” IEEE Trans. Knowl. Data Eng., vol. 25, no. 5, pp. 961–973, May 2013.

Y. Huang et al., “Telco churn prediction with big data,” in Proc. ACM SIGMOD Int. Conf. Manage. Data, San Francisco, CA, USA, 2015, pp. 607–618.

C. L. Chen and CY. Zhang, “Data-intensive applications, challenges, techniques and technologies: A survey on big data,” Inf. Sci., vol. 275, pp. 314–347, Aug. 2014.

H. Li, D. Wu, and G. X. Li, “Enhancing telco service quality with big data enabled churn analysis: Infrastructure, model, and deployment,” J. Comput. Sci. Technol., vol. 30, no. 6, pp. 1201–1214, Nov. 2015.

X. D. Liu, “A new mathematical axiomatic system of fuzzy sets and systems,” Int. J. Fuzzy Math., vol. 3, pp. 559–560, 1995.

X. D. Liu, “The fuzzy sets and systems based on AFS structure, EI algebra and EII algebra,” Fuzzy Sets Syst., vol. 95, no. 2, pp. 179–188, Apr. 1998.

J. Dean and S. Ghemawat, “MapReduce: Simplified data processing on large clusters,” Commun. ACM, vol. 51, no. 1, pp. 107–113, 2008.

Y. J. Xu, W. Y. Qu, Z. Y. Li, and G. Y. Min, “Efficient-means++ approximation with MapReduce,” IEEE Trans. Parallel Distrib. Syst., vol. 25, no. 12, pp. 3135–3144, Dec. 2014.

I. Klevecka and J. Lelis, “Pre-processing of input data of neural networks: The case of forecasting telecommunication network traffic,” Telektronikk: Telecommun. Forecast. vol. 104, no. 3/4, pp. 168–178, 2008.


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