In view of the fact that clustering algorithms are widely used in the financial field, this paper compares and analyzes the three clustering algorithms, DBSCAN, K-means and X-means, in terms of execution efficiency, scalability, and outlier detection capabilities based on a bank customer data set, and proposes to apply the X-means algorithm to customer segmentation in the banking industry. A bank customer segmentation model is established using the X-means algorithm to provide scientific decision support for bank decision makers. Keywords: clustering; K-means algorithm; X-means algorithm; customer segmentation
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