Improved Clustreing Algorithm based on Banking

Authors

  • vijay saini Department of Comp. Sc. & Engg. Lovely Professional University, Phagwara
  • Arko Bagchi Assistant Professor, Department of Comp. Sc. & Engg. Lovely Professional University, Phagwara
  • Gurpreet Singh Assistant Professor, Department of Comp. Sc. & Engg, SBBSIET, Padhiana.

DOI:

https://doi.org/10.17722/ijrbt.v5i1.215

Keywords:

Data mining, k-mean, self-organizing map, clustering, cluster analysis, Types of data mining algorithms.

Abstract

This research can handle the large data set of banking related to customers. It can help us for reduce computation time and increase efficiency of data set related to banking. We can use clustering technique to distribute objects of similar type and extract knowledge from database. We can provide best services to deal with different customers. We checks out which algorithm produce better quality results as compare to previous algorithm. K mean algorithm is used in real applications. The proposed algorithm calculates minimum distance between objects using data set and compares it with previous k mean algorithm. Our algorithm improve accuracy of data set as compare to previous k mean algorithm.

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Published

2014-08-31

Issue

Vol. 5 No. 1 (2014): International Journal of Research in Business and Technology

Section

Comparative Studies