<![CDATA[The rapid growth of digital libraries has generated increasingly large borrowing transaction data, creating the need for analytical techniques to understand user behavior patterns and support data-driven library management. This study aims to cluster library users based on their borrowing behavior using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to identify user segments according to their borrowing activity. The study employed the Book-Crossing Dataset, consisting of 278,858 rating transactions, 271,379 user records, and 271,360 book records. The research methodology included Exploratory Data Analysis (EDA), data preprocessing, feature engineering, feature standardization using StandardScaler, ε parameter selection through the K-Distance Graph, DBSCAN clustering, cluster evaluation using the Silhouette Score, and visualization using Principal Component Analysis (PCA). The experimental results indicate that ε = 0.5 and MinPts = 5 produced three clusters, with 244 users identified as noise. The obtained Silhouette Score of 0.5996 demonstrates a reasonably good clustering quality. Furthermore, the resulting clusters successfully represent users with low, moderate, and very high borrowing activities, providing valuable insights for developing personalized library services, improving book recommendation systems, supporting collection development, and facilitating data-driven decision-making to enhance the overall quality of digital library services.]]>
