Customer Segmentation in XYZ Bank Using K-Means and K-Medoids Clustering | IEEE Conference Publication | IEEE Xplore

Customer Segmentation in XYZ Bank Using K-Means and K-Medoids Clustering


Abstract:

The Internet banking customers has growth very fast. Customer segmentation can be applied based on Internet banking data. Clustering is unsupervised data mining technique...Show More

Abstract:

The Internet banking customers has growth very fast. Customer segmentation can be applied based on Internet banking data. Clustering is unsupervised data mining technique that can be used for customer segmentation. This research build clustering models on customer profile data based on their usage of internet Banking in XYZ bank. The clustering methods employed K-Means method and K-Medoids method based on RFM score of customer's Internet Banking transactions. This research used Knowledge Discovery methodology [18]. The performances of both methods were measured and compared. The result shows that K-Means method outperformed K-Medoids method based on intra cluster (A WC) distance. While based on Davies-Bouldin index, K-Means performs slightly better than K-Medoids.
Date of Conference: 03-05 September 2018
Date Added to IEEE Xplore: 11 November 2018
ISBN Information:
Conference Location: Jakarta, Indonesia

Contact IEEE to Subscribe

References

References is not available for this document.