Customer Segmentation in XYZ Bank Using K-Means and K-Medoids Clustering
Authors:
- 1 September 2018
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 412-416
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.Keywords
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