Comparing clustering models in bank customers: Based on Fuzzy relational clustering approach

Abstract: Clustering is absolutely useful information to explore data structures and has been employed in many places. It organizes a set of objects into similar groups called clusters, and the objects within one cluster are both highly similar and dissimilar with the objects in other clusters. The K-mean, C-mean, Fuzzy C-mean and Kernel K-mean algorithms are the most popular clustering algorithms for their easy implementation and fast work, but in some cases we cannot use these algorithms. Regarding this, in this paper, a hybrid model for customer clustering is presented that is applicable in five banks of Fars Province, Shiraz, Iran. In this way, the fuzzy relation among customers is defined by using their features described in linguistic and quantitative variables. As follows, the customers of banks are grouped according to K-mean, C-mean, Fuzzy C-mean and Kernel K-mean algorithms and the proposed Fuzzy Relation Clustering (FRC) algorithm. The aim of this paper is to show how to choose the best clustering algorithms based on density-based clustering and present a new clustering algorithm for both crisp and fuzzy variables. Finally, we apply the proposed approach to five datasets of customer's segmentation in banks. The result of the FCR shows the accuracy and high performance of FRC compared other clustering methods.

How to cite this paper

 Hendalianpour, A., Razmi, J & Gheitasi, M. (2017). Comparing clustering models in bank customers: Based on Fuzzy relational clustering approach.Accounting, 3(2), 81-94.

Refrences

Akman, G. (2015). Evaluating suppliers to include green supplier development programs via fuzzy c-means and VIKOR methods. Computers & Industrial Engineering, 86, 69-82.
Aliguliyev, R. M. (2009). Performance evaluation of density-based clustering methods. Information Sciences, 179(20), 3583-3602.
Chehreghani, M. H., Abolhassani, H., & Chehreghani, M. H. (2009). Density link-based methods for clustering web pages. Decision Support Systems,47(4), 374-382.
Clir, G. J., & Yuan, B. (1995). Fuzzy sets and fuzzy logic. Theory and application. Prentice Hall PTR.
Eberle, D. G., Daudi, E. X., Muiuane, E. A., Nyabeze, P., & Pontavida, A. M. (2012). Crisp clustering of airborne geophysical data from the Alto Ligonha pegmatite field, northeastern Mozambique, to predict zones of increased rare earth element potential. Journal of African Earth Sciences,62(1), 26-34.
Feng, L., Qiu, M. H., Wang, Y. X., Xiang, Q. L., Yang, Y. F., & Liu, K. (2010). A fast divisive clustering algorithm using an improved discrete particle swarm optimizer. Pattern Recognition Letters, 31(11), 1216-1225.
Filippone, M., Camastra, F., Masulli, F., & Rovetta, S. (2008). A survey of kernel and spectral methods for clustering. Pattern recognition, 41(1), 176-190.
Höppner, F. (1999). Fuzzy cluster analysis: methods for classification, data analysis and image recognition. John Wiley & Sons.
Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern recognition letters, 31(8), 651-666.
Jiang, H., Yi, S., Li, J., Yang, F., & Hu, X. (2010). Ant clustering algorithm with K-harmonic means clustering. Expert Systems with Applications,37(12), 8679-8684.
Jiménez-García, R., Esteban-Hernández, J., Hernández-Barrera, V., Jimenez-Trujillo, I., López-de-Andrés, A., & Garrido, P. C. (2011). Clustering of unhealthy lifestyle behaviors is associated with nonadherence to clinical preventive recommendations among adults with diabetes. Journal of Diabetes and its Complications, 25(2), 107-113.
Kannappan, A., Tamilarasi, A., & Papageorgiou, E. I. (2011). Analyzing the performance of fuzzy cognitive maps with non-linear hebbian learning algorithm in predicting autistic disorder. Expert Systems with Applications,38(3), 1282-1292.
Khan, S. S., & Ahmad, A. (2004). Cluster center initialization algorithm for K-means clustering. Pattern recognition letters, 25(11), 1293-1302.
Lee, J. W., Yeung, D. S., & Tsang, E. C. (2005). Hierarchical clustering based on ordinal consistency. Pattern recognition, 38(11), 1913-1925.
Liang, G. S., Chou, T. Y., & Han, T. C. (2005). Cluster analysis based on fuzzy equivalence relation. European Journal of Operational Research,166(1), 160-171.
Núñez, A., De Schutter, B., Sáez, D., & Škrjanc, I. (2014). Hybrid-fuzzy modeling and identification. Applied Soft Computing, 17, 67-78.
Peters, G., Crespo, F., Lingras, P., & Weber, R. (2013). Soft clustering–fuzzy and rough approaches and their extensions and derivatives. International Journal of Approximate Reasoning, 54(2), 307-322.
Pedrycz, W., & Rai, P. (2008). Collaborative clustering with the use of Fuzzy C-Means and its quantification. Fuzzy Sets and Systems, 159(18), 2399-2427.
Ravi, V., & Zimmermann, H. J. (2000). Fuzzy rule based classification with FeatureSelector and modified threshold accepting. European Journal of Operational Research, 123(1), 16-28.
Rose, K. (1998). Deterministic annealing for clustering, compression, classification, regression, and related optimization problems. Proceedings of the IEEE, 86(11), 2210-2239.
Schölkopf, B., Smola, A., & Müller, K. R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural computation, 10(5), 1299-1319.
Wu, J., Chen, J., Xiong, H., & Xie, M. (2009). External validation measures for K-means clustering: A data distribution perspective. Expert Systems with Applications, 36(3), 6050-6061.
Zhao, Y., & Karypis, G. (2004). Empirical and theoretical comparisons of selected criterion functions for document clustering. Machine Learning,55(3), 311-331.
Zimmermann, H. J. (1996). Fuzzy Control. In Fuzzy Set Theory—and Its Applications (pp. 203-240). Springer Netherlands.

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