Fuzzy C-Mean Technique for Accessing Large Database of Banking Sector

Authors

  • Banshidhar Choudhary Department of Computer Science Babasaheb Bhimrao Ambedkar University, Lucknow, India, 226025
  • Vipin Saxena Department of Computer Science Babasaheb Bhimrao Ambedkar University, Lucknow, India, 226025

Keywords:

Fuzzy C-Means, K-Means, Banking Data, Python and R Language

Abstract

The Fuzzy C-Means (FCM) technique has gained significant attention in the field of data analysis and clustering due to its ability to handle complex and ambiguous data sets. In this paper, FCM technique is applied to banking data using Python and R programming languages. The objective of this study is to explore the potential of FCM in clustering of the banking data and to evaluate its performance in comparison to K-Means clustering algorithm. The paper begins by providing an overview of the FCM algorithm and its underlying principle. Thereafter, the process of preprocessing and preparing the banking data for analysis is done, further FCM algorithm in both python and R have implemented after utilizing the respective libraries and packages. The computed results are compared with widely used clustering algorithms, such as K-Means and Hierarchical clustering.

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Published

21.09.2023

How to Cite

Choudhary, B. ., & Saxena, V. . (2023). Fuzzy C-Mean Technique for Accessing Large Database of Banking Sector. International Journal of Intelligent Systems and Applications in Engineering, 11(4), 263–271. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3521

Issue

Vol. 11 No. 4 (2023)

Section

Research Article

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