Optimized Methodology for Hassle-Free Clustering of Customer Issues in Banking

  • G. Naveen SundarEmail author
  • D. Narmadha
  • S. Jebapriya
  • M. Malathy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 768)


The unprecedented growth of issues generated in banking sector is extremely huge. It is important to prevent customer churn by retaining existing customers and acquiring new customers so that is important for analyzing. Since data stored in the databases of banks are generally complex and are of varying dimensions such as consumer loan, debt collection, credit reporting and mortgage, the procedure for data analysis becomes very difficult. This paper presents a simplified framework for clustering the various issues by using a combination of data mining techniques. Hence in huge datasets issues from recorded by the customers are clustered using an efficient clustering algorithm. The parameters such as execution time and prediction accuracy are used to compare the results of the algorithms.


Decision tree MapReduce 


  1. 1.
    Gionis, A., Indyky, P., Motwaniz, R.: Similarity search in high dimensions via hashing. In: Proceedings of the 25th VLDB Conference, Edinburgh, Scotland (1999)Google Scholar
  2. 2.
    Jiang, J.Y., Liou, R.J., Lee, S.J.: A fuzzy self-constructing feature clustering algorithm for text classification. IEEE Trans. Knowl. Data Eng. 23(3), 335–349 (2011)Google Scholar
  3. 3.
    Jacob, S.G., Ramani, R.G.: Evolving efficient clustering and classification patterns in lymphography data through data mining techniques. Int. J. Soft Comput. (IJSC) 3(3) (2012)Google Scholar
  4. 4.
    Klenk, S., Dippon, J., Fritz, P., Heidemann, G.: Determining patient similarity in medical social networks. In: Proceedings of the MEDEX 2010 (2010)Google Scholar
  5. 5.
    Fahim, A.M., Salem, A.M., Torkey, F.A., Ramadan, M.A.: An efficient enhanced k-means clustering algorithm. J. Zhejiang Univ. Sci. (ISSN 1009–3095, ISSN 1862-1775) (2006)Google Scholar
  6. 6.
    Deng, D., Li, G., Hao, S., Wang, J., Feng, J., Li, W.S., Join, M.: A mapreduce-based method for scalable string similarity joins. In: 30th International Conference on Data Engineering(ICDE), China (2014)Google Scholar
  7. 7.
    Al-Taani, A.T., Al-Awad, N.A.K.: A comparative study of web-pages classification methods using fuzzy operators applied to arabic web-pages. World Academy of Science, Engineering and Technology. Int. J. Comput. Electr. Autom. Control Inf. Eng. 1(7) (2007)Google Scholar
  8. 8.
    Guelpeli, M.V.C., Garcia, A.C.B.: An analysis of constructed categories for textual classification using fuzzy similarity and agglomerative hierarchical methods. In: Third International IEEE Conference Signal-Image Technologies and Internet-Based System (SITIS), pp. 92–99 (2007)Google Scholar
  9. 9.
    Rostam Niakan Kalhori, M., Fazel Zarandi, M.H., Turksen, I.B.: A new credibilistic clustering algorithm. Inf. Sci. 279, 105–122 (2014)Google Scholar
  10. 10.
    Wu, X., Wu, B., Sun, J., Qiu, S., Li, X.: A hybrid fuzzy K-harmonic means clustering algorithm. Appl. Math. Model. 39, 3398–3409 (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • G. Naveen Sundar
    • 1
    Email author
  • D. Narmadha
    • 1
  • S. Jebapriya
    • 1
  • M. Malathy
    • 2
  1. 1.Karunya Institute of Technology and Sciences (KITS)CoimbatoreIndia
  2. 2.VelTech High Tech (VTHT)ChennaiIndia

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