Comparison of Hierarchical, K-Means and DBSCAN Clustering Methods for Credit Card Customer Segmentation Analysis Based on Expenditure Level

Abstract

The amount of data from credit card users is increasing from year to year. Credit cards are an important need for people to make payments. The increasing number of credit card users is because it is considered more effective and efficient. The third method used today has a function to determine the effective outcome of credit card user scenarios. In this study, a comparison was made using the Hierarchical Clustering, K-Means and DBSCAN methods to determine the results of credit card customer segmentation analysis to be used as a market strategy. The results obtained based on the best silhouette coefficient score method is two cluster hierarchical clustering with 0.82322 score. Based on the best mean value customers are divided into two segments, and it is suggested to develop strategies for both segments.