Customer Segmentation of Bank based on Data Mining – Security Value based Heuristic Approach as a Replacement to K-means Segmentation

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International Journal of Computer Applications
© 2011 by IJCA Journal
Number 8 - Article 3
Year of Publication: 2011
Authors:
Shashidhar HV
Subramanian Varadarajan
10.5120/2383-3145

Shashidhar HV and Subramanian Varadarajan. Article: Customer Segmentation of Bank based on Data Mining Security Value based Heuristic Approach as a Replacement to K-means Segmentation. International Journal of Computer Applications 19(8):13-18, April 2011. Full text available. BibTeX

@article{key:article,
	author = {Shashidhar HV and Subramanian Varadarajan},
	title = {Article: Customer Segmentation of Bank based on Data Mining  Security Value based Heuristic Approach as a Replacement to K-means Segmentation},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {19},
	number = {8},
	pages = {13-18},
	month = {April},
	note = {Full text available}
}

Abstract

K-means segmentation algorithm can be applied to Customer Segmentation in Banks. If loan over-due amount of bank customers are normally distributed, then K-means can be used. In cases of significant outliers, K-means segmentation algorithm cannot be applied. In our proposed solution, bank loan customers are segmented based on security value and loan over-due amount. Proposed solution addresses segmentation issues on outliers and provides security value based heuristic approach as a replacement to K-means segmentation.

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