Clustering Of Library’s Patron Behavior Using Machine Learning
DOI:
https://doi.org/10.31849/digitalzone.v16i1.19680Keywords:
Patron Behavior, Machine Learning, University Library, clusteringAbstract
Libraries collect a lot of important transaction data, but they rarely use this information to improve how consumers interact with them. This work tries to bridge this gap by offering a novel use of machine learning to analyze and classify library patron behavior. Customers were categorized based on their age range, checkouts, and renewals using the KMeans clustering technique. Dimensionality reduction methods like PCA and t-SNE were used to visually clarify the generated patterns. Our research revealed three different user groups: Rare Borrowers, who typically make 5.4 checkouts and 2.0 renewals; Occasional Borrowers, who typically make 20.8 checkouts and 7.1 renewals; and Frequent Borrowers, who often make 50.3 checkouts and 15.4 renewals. The clustering model performed quite well, as evidenced by its Calinski-Harabasz Index of 320.12, Davies-Bouldin Index of 0.45, and Silhouette Score of 0.62. Beyond these metrics, the study’s novelty lies in its practical implications—offering libraries a data-driven framework to tailor services, improve user satisfaction, and optimize resource allocation. This study highlights the transformative potential of machine learning in library science offering a data-driven framework for libraries to personalize services, optimize book recommendations, and enhance outreach efforts based on patron behavior. By segmenting users, libraries can better allocate resources and improve user experience. Limitation of this study lies on the data bias which may affect generalizability due to demographic differences across libraries. Additionally, KMeans clustering assumes predefined clusters, which may not fully capture nuanced behaviors.
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