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

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

Abstract

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.

Keywords

Decision tree MapReduce 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • G. Naveen Sundar
    • 1
  • 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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