A Prospectively Validated K-Means Framework for Demand Tiering and Acquisition Prioritisation in Academic Library Collections

Jadhav, Yuvraj G.; More, Rajshri R

doi:10.22034/kps.2026.595592.1296

A Prospectively Validated K-Means Framework for Demand Tiering and Acquisition Prioritisation in Academic Library Collections

Document Type : Original Research Manuscripts

Authors

Yuvraj G. Jadhav ¹

Rajshri R More ²

¹ Associate Professor in the Department of Library and Information Science, Shivaji University, Kolhapur, Maharashtra, India.

² Librarian, Gopal Krishna Gokhale College, Kolhapur, Maharashtra, India.

10.22034/kps.2026.595592.1296

Abstract

Though academic libraries have access to wide-ranging circulation data‚ collection decisions are still typically based on descriptive use reports‚ which have shown an inability to identify sustained demand titles‚ or anticipate demand for titles․ A data-driven approach to create ordered demand tiers‚ and an ordered acquisition priorities list‚ from standard circulation data is proposed and validated․ The circulation data for the period June 2024 to June 2026 was collected from the Knowledge Resource Centre‚ SNDT Women's University‚ Pune․ A total of 4984 transactions on 2615 titles and 915 borrowers were analyzed․ Five title-level circulation indicators were employed: total circulation transactions‚ unique borrowers‚ active months‚ transaction per active month‚ and renewals․ The consecutive design involved computing features on the date range of January to December 2025 based on 1‚626 titles‚ and testing prediction on January to April 2026 circulation levels․ We compared K-Means clustering with Ward hierarchical clustering and DBSCAN using four internal indices and a stability test based on 30 random initialisations․ A composite demand score was calculated for the indicators after normalization using four weighting schemes․ However‚ performance of the composite score was similar to a simple transaction-count baseline․ Therefore‚ the main benefit of the composite score was its use of an interpretable tier structure‚ rather than improved ranking performance․ It provides a framework for reproducible acquisition prioritization‚ duplicate-copy planning‚ and identifying underused holdings․

Keywords

Library circulation analysis

K-Means clustering

Demand tiering

Collection development

Temporal validation

Decision support

Subjects

Data mining

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