Abstract
Analysis of customer risk is important task in business operation of the enterprise. This paper, first proposes the building of strength measure to rank clusters obtained from clustering algorithm. The next step builds an improved clustering algorithm by integrating of computing this measure into a clustering algorithm. This improved clustering algorithm is named Fuzzy C-Means-Rank (FCM-R). The final step proposes a hybrid data mining method to analyze the risk of the customer. The hybrid method includes two stages. In the first stage, we apply a classifying algorithm to classify customers into two groups: risk and no risk. In the second stage, we apply the improved clustering algorithm FCM-R to cluster risk customers into clusters and to rank clusters by measuring the risk level of clusters. The hybrid method has been applied to a real data set to generate clusters ranked according to the risk level from high to low. With such results, our proposed method will support enterprises to analyze customer risk and to propose appropriate risk management regulation for customers in each cluster.
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References
Doan, H., Nguyen, D.T.: An adaptive method to determine the number of clusters in clustering process. In: Proceedings of The International Conference on Computer and Information Sciences, ICCOINS 2014, June 3–5, 2014, IEEE, Malaysia, pp. 1–6, ISBN 978-1-4799-4391-3 (2014)
Nguyen, D.T., Doan, H.: An approach to determine the number of clusters for clustering algorithms. ICCCI 2012, Part I, LNAI 7653. Springer, Heidelberg, pp. 485–494 (2012)
Bezdek, J.C., Ehrlich, R., Full, W.: FCM: The fuzzy c-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)
Hathaway, R.J., Bezdek, J.C.: Recent convergence results for the fuzzy c-means clustering algorithms. J. Classif. 5, 237–247 (1988)
http://www.rulequest.com/see5-info.html. Accessed May 2014
Bar-Yossef, Z., Guy, I., Lempel, R., Maarek, Y.S., Soroka, V.: Cluster ranking with an application to mining mailbox networks. Knowl. Inf. Syst. 14(1), 101–139 (2008)
Shang, Y.: Consensus formation of two-level opinion dynamics. Acta Math. Sci. 34(4), 1029–1040 (2014)
Hanafizadeh, P., Paydar, N.R.: A data mining model for risk assessment and customer segmentation in the insurance industry. Int. J. Strateg. Decis. Sci. 4(1), 52–78 (2013)
Cheng, C.-H., Chen, Y.-S.: Classifying the segmentation of customer value via RFM model and RS theory. Expert Syst. Appl. 36(2009), 4176–4184 (2009)
Liang, Y.-H.: Integration of data mining technologies to analyze customer value for the automotive maintenance industry. Expert Syst. Appl. 37, 7489–7496 (2010)
Farajian, M.A., Mohammadi, S.: Mining the banking customer behavior using clustering and association rules methods. Int. J. Ind. Eng. Prod. Res. 21(4), 239–245 (2010)
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Doan, H., Nguyen, D.T., Ho, B.Q. (2016). Building a Measure to Integrate into a Hybrid Data Mining Method to Analyze the Risk of Customer. In: Sulaiman, H., Othman, M., Othman, M., Rahim, Y., Pee, N. (eds) Advanced Computer and Communication Engineering Technology. Lecture Notes in Electrical Engineering, vol 362. Springer, Cham. https://doi.org/10.1007/978-3-319-24584-3_71
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DOI: https://doi.org/10.1007/978-3-319-24584-3_71
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