Enhancing library book recommendations using clustering and association rule mining
DOI:
https://doi.org/10.47989/ir31363451Keywords:
Library, Book recommendation, Association rule mining, K-means clustering, FP-Growth algorithmAbstract
Introduction. This study investigates library user clusters to improve book recommendations. Data on daily borrowing behaviours from 2020 to 2025 were collected from the Prince of Songkla University, Pattani Campus library.
Method. Users were clustered using K-means. The optimal number of clusters was selected via the elbow method combined with silhouette scores. Borrowing-pattern relationships were analysed using association rule mining with the FP-Growth algorithm (minimum support 15%, minimum confidence 90%).
Analysis. The study examined the distribution of borrowed book categories and evaluated clustering performance using precision, recall, F-measure, and hit rate. Association rules were extracted to identify co-borrowing trends among categories.
Results. Languages was the most borrowed category, followed by History & Geography and Science. The best user clustering solution was k = 3, achieving precision 82.98%, recall 98.55%, F-measure 0.8815, and hit rate 97.57%. The strongest rule showed that users borrowing Economics [330], Social Sciences [300], and History of Europe [940] were likely to also borrow History of Asia [950]. Associations were also found among life sciences, biology, physics, and chemistry.
Conclusions. The study results indicate patterns of interest and knowledge connections derived from borrowing behaviour, which can be used to improve library services and serve as a model for data-driven library management.
References
Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD Record, 22(2), 207–216. https://doi.org/10.1145/170036.170072.
Alaoui, D., Riffi, J., Sabri, M. A., Aghoutane, B., Yahyaouy, A., & Tairi, H. (2024). Comparative study of filtering methods for scientific research article recommendations. Big Data and Cognitive Computing, 8(12), 190–190. https://doi.org/10.3390/bdcc8120190.
Amin, F. M., Rusydiyah, E. F., &Azizah, A. N. (2025).Personalized library book recommendations using K-Means clustering and association rules. Journal of Scientometric Research, 14, 32–45. https://doi.org/10.5530/jscires.20251005 .
Anitha, P., & Patil, M. M. (2022). RFM model for customer purchase behavior using K-Means algorithm. Journal of King Saud University - Computer and Information Sciences, 34(5), 1785-1792. https://doi.org/10.1016/j.jksuci.2019.12.011
Annisa, S., Rini, D. P., & Abdiansah, A. (2024). Collaborative filtering recommendation system using a combination of clustering and association rule mining. Journal of Information Systems and Informatics, 6(3), 1499–1516. https://doi.org/10.51519/journalisi.v6i3.802 .
Baka, A., Persoh, S., Latekeh, I., Abdulyasat, A., Pangtip, P., &Deumong, F. (2022). Data mining techniques by using association rules for purchasing behavior analysis of retail customers: A case study of D2Shop, Pattani province. Journal of Technology Management Rajabhat Maha Sarakham University, 9(2), 18–29. https://ph02.tci-thaijo.org/index.php/itm-journal/article/view/247578.
Bargagli Stoffi, F. J., Cevolani, G., & Gnecco, G. (2022). Simple models in complex worlds: Occam’s Razor and statistical learning theory. Minds & Machines, 32, 13–42. https://doi.org/10.1007/s11023-022-09592-z.
Bhajantri, A., Nagesh, K., Goudar, R. H., Dhananjaya, G. M., Kaliwal, R. B., Rathod, V. N., Kulkarni, A., & Govindaraja, K. (2024). Personalized book recommendations: A hybrid approach leveraging collaborative filtering, association rule mining, and content-based filtering. EAI Endorsed Transactions on Internet of Things, 10. https://doi.org/10.4108/eetiot.6996
Bhatia, P. (2019).Data mining and data warehousing: Principles and practical techniques. Cambridge University Press. https://doi.org/10.1017/9781108635592.
Christy, A. J., Umamakeswari, A., Priyatharsini, L., &Neyaa, A. (2021). RFM ranking: An effective approach to customer segmentation. Journal of King Saud University - Computer and Information Sciences, 33(10), 1251-1257. https://doi.org/10.1016/j.jksuci.2018.09.004 .
Cox, A. M., & Mazumdar, S. (2022). Defining artificial intelligence for librarians. Journal of Librarianship and Information Science, 56(2), 330–340. https://doi.org/10.1177/09610006221142029
Cui, M. (2020). Introduction to the K-Means clustering algorithm based on the elbow method. Geoscience and Remote Sensing, 3, 9–16. https://doi.org/10.23977/accaf.2020.010102.
Duchen, H. (2022). A comparative study of various book recommendation algorithms for public libraries. Technical Services Quarterly, 39(4), 369–380. https://doi.org/10.1080/07317131.2022.2125676.
Fan, S., Xu, Y., Zhu, B., et al. (2022). Student behavior analysis research of based on data mining. The 2nd International Conference on Big Data, Artificial Intelligence and Risk Management (ICBAR), Xi'an, China, November 25-27, 2022 (pp. 130–134). IEEE. https://doi.org/10.1109/ICBAR58199.2022.00032.
FIRAT, İ., & Aslay, F. (2022). Knowledge Discovery from Library Automation via Bibliomining using the Apriori Algorithm. Gazi University Journal of Science, 35(4), 1344–1357. https://doi.org/10.35378/gujs.983642 .
Guo, Y. (2021). CNS: interactive intelligent analysis of financial management software based on apriori data mining algorithm. International Journal of Cooperative Information Systems, 30(01n04), 2150008. https://doi.org/10.1142/S0218843021500088 .
Guzman, E. E. C., Tano, I. M., Piad, K., Lagman, A. C., Espino, J., Mababa, J., & Victoriano, J. M. (2025). Unlocking insights from academic library data using clustering and recommender dashboard analytics for enhanced book collection management. World Journal of Advanced Research and Reviews, 25(3), 645–657. https://doi.org/10.30574/wjarr.2025.25.3.0779.
Ikwuanusi, U. F., Onunka, O., Owoade, S., & Uzoka, A. C. (2024). Revolutionizing library systems with advanced automation: A blueprint for efficiency in academic resource management. International Journal of Scholarly Research in Multidisciplinary Studies, 5(2), 19–40. https://doi.org/10.56781/ijsrms.2024.5.2.0045.
Isnain, A. R., Supriyanto, J., &Kharisma, M. P. (2021). Implementation of the K-Nearest Neighbor (K-NN) algorithm for public sentiment analysis of online learning. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 15(2), 121–130. https://doi.org/10.22146/ijccs.65176.
Jiwitan, N. (2022). The discovery of association rules and data analysis to optimize Japanese food sales. Journal of Information Science and Technology, 12(1), 1–12. https://doi.org/10.14456/jist.2022.1.
Kajornkasirat, S., Hnusuwan, B., Puttinaovarat, s., Puangsuwan, K., & Kaewsuwan, N. (2025). Recommender system for dengue prevention using machine learning. IAES International Journal of Artificial Intelligence, 14(2), 1106–1115. https://doi.org/10.11591/ijai.v14.i2.pp1106-1115.
Khademizadeh, S., Nematollahi, Z., &Danesh, F. (2022). Analysis of book circulation data and a book recommendation system in academic libraries using data mining techniques. Library & Information Science Research, 44(4), 101191. https://doi.org/10.1016/j.lisr.2022.101191.
Kortemeyer, G., & Dröschler, S. (2021). A user-transaction- based recommendation strategy for an educational digital library. International Journal on Digital Libraries, 22(2), 147–157. https://doi.org/10.1007/s00799-021-00298-8 .
Lael, T. A. D., & Pramudito, D. A. (2023). Use of Data Mining for the analysis of consumer purchase patterns with the Fp growth algorithm on motor spare part sales transactions data. IAIC Transactions on Sustainable Digital Innovation (ITSDI), 4(2), 128–136. https://doi.org/10.34306/itsdi.v4i2.582 .
Lakshika, T., & Caldera, A. (2022). Association Rules for knowledge discovery from e-news articles: A review of Apriori and FP-Growth algorithms. Advances in Science Technology and Engineering Systems Journal, 7(5), 178–192. https://doi.org/10.25046/aj070519.
Liao, S., Widowati, R., & Hsieh, Y. (2021). Investigating online social media users’ behaviors for social commerce recommendations. Technology in Society, 66(2021), 1–14. https://doi.org/10.1016/j.techsoc.2021.101655.
Liu, Y. (2018). Data mining of university library management based on improved collaborative filtering association rules algorithm. Wireless Personal Communications, 102(4), 3781–3790. https://doi.org/10.1007/s11277-018-5409-y .
Makawana, M., &Mehta, R. G. (2023). Discovering search space using M-distance clustering of semantic relatedness based weighted network for the content-based recommender system. Journal of Scientometric Research, 12(2), 243–253. https://doi.org/10.5530/jscires.12.2.024.
Mamoriya, A. (2021). Open-source software and proprietary software for library automation. Journal of Advanced Research in Library and Information Science, 8(2), 1–3. https://doi.org/10.24321/2395.2288.202101.
Mariana, S., Surjandari, I., Dhini, A., Rosyidah, A., & Prameswari, P. (2017). Association rule mining for building book recommendation system in online public access catalog. 2017 3rd International Conference on Science in Information Technology (ICSITech), 246–250. https://doi.org/10.1109/ICSITech.2017.8257119.
Mao, Z., Wu, J., Qiao, Y., & Yao, H. (2021). Government data governance framework based on a data middle platform. Aslib Journal of Information Management, 74(2), 289–310. https://doi.org/10.1108/AJIM-03-2021-0068.
Mohanty, B., Tripathy, M., & Champati, S. (2023). Performance Analysis of Association Rule Mining Algorithms: Evidence from the Retailing Industry. Journal of Engineering Science and Technology Review, 16(5), 108–122. https://doi.org/10.25103/jestr.165.14.
Monika, W., Nasution, A. H., Syam, F. A., & Wijesundara, C. (2025). Clustering Of Library’s Patron behavior Using Machine Learning. Digital Zone Jurnal Teknolog iInformasi DanKomunikasi, 16(1), 1–11. https://doi.org/10.31849/digitalzone.v16i1.19680.
Phoemsangwuwan, P., Keawmen, K., &Muenpo, S. (2022). Assessment of information technology services and analyse of the behavior of using information technology resources of the library for educational quality assurance: a case study of Surasak Srisuk Library, Mahidol University. TLA Bulletin, 66(1), 104–118. https://so06.tci-thaijo.org/index.php/tla_bulletin/article/view/255954.
Puritat, K., & Intawong, K. (2020). Development of an open source automated library system with book recommendation system for small libraries. 2020 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), Pattaya, Thailand, March 11-214, 2020 (pp. 128–132). IEEE. https://doi.org/10.1109/ECTIDAMTNCON48261.2020.9090753.
Puritat, K., Julrode, P., Ariya, P., Sangamuang, S., & Intawong, K. (2021). Book recommendation for library automation use in school libraries by multi features of support vector machine. International Journal of Advanced Computer Science and Applications, 12(4). https://doi.org/10.14569/ijacsa.2021.0120426
Qisman, M. Rosadi, R., & Abdullah, A. S. (2021). Market basket analysis using apriori algorithm to find consumer patterns in buying goods through transaction data (case study of Mizan computer retail stores). Journal of Physics: Conference Series, 1722(2021). https://doi.org/10.1088/1742-6596/1722/1/012020.
Safira, W. E., & Mohammed, S. (2026). Supporting academic library collection decisions using K-Means–based book recommendation. Artificial Intelligence in Educational Decision Sciences, 1(2), 22–33. https://doi.org/10.66053/aieds.v1i2.25.
Silwattananusarn, T., Kulkanjanapiban, P., & Athakravisunthorn, K. L. (2020). Mining and analyzing patron’s book-loan data and university data to understand library use patterns. International Journal of Information Science and Management, 18(2), 151–172. https://doi.org/10.48550/arXiv.2008.03545.
Singh, P. K., Othman, E., Ahmed, R, Mahmood, A., Dhahri, H., & Choudhury, P. (2021).Optimized recommendations by user profiling using apriori algorithm. Applied Soft Computing, 106, 107272. https://doi.org/10.1016/j.asoc.2021.107272.
Smith, T. (2023, May 4). Library funding becomes the “nuclear option” as the battle over books escalates. NPR. https://www.npr.org/2023/05/04/1173274834/book-bans-library-funding-missouri-texas-ashcroft (Archived by the Internet Archive at https://web.archive.org/web/20230621222422/https://www.npr.org/2023/05/04/1173274834/book-bans-library-funding-missouri-texas-ashcroft)
Sundari, P. S., &Subaji, M. (2022). An improved hidden behavioral pattern mining approach to enhance the performance of recommendation system in a big data environment. Journal of King Saud University - Computer and Information Sciences, 34(10, Part A), 8390–8400. https://doi.org/10.1016/j.jksuci.2020.09.010.
Tinpun, N., Rumdon, K., Kaewsuwan, N., Theppaya, T., Matcha, W., Kammanee, K., & Khammanee, W. (2025). Using the association rule to analyze the book borrowing behavior of Prince of Songkla University students with data mining techniques. Journal of Information Science Research and Practice, 43(3), 23–41. https://doi.org/10.14456/jiskku.2025.16.
Kajornkasirat, S., Hnusuwan, B., Puttinaovarat, s., Puangsuwan, K., & Kaewsuwan, N. (2025). Recommender system for dengue prevention using machine learning. IAES International Journal of Artificial Intelligence, 14(2), 1106–1115.
Van Eeden, E. S. (2024). Considering the many variations of regional history. Histories of People and Place, 1–22. https://doi.org/10.1080/28334299.2024.2433919.
W3Schools. (2025). Machine learning: K-means. W3Schools. https://www.w3schools.com/python/python_ml_k-means.asp (Archived by the Internet Archive at https://web.archive.org/web/20250617131157/https://www.w3schools.com/python/python_ml_k-means.asp)
Wang, C., You, F., &Wang, Y. (2025). Text intelligent classification model based on improved KNN algorithm in library service transformation. Systems and Soft Computing, 7, 200313. https://doi.org/10.1016/j.sasc.2025.200313.
Wang, J., Alroobaea, R., Baqasah, A. M., Althobaiti, A., &Kansal, L.(2022). Study on library management system based on data mining and clustering algorithm. Informatica (Slovenia), 46(9), 17–24. https://doi.org/10.31449/inf.v46i9.3858.
Wu, S., Lin, M., Ji, M., & Wang, T. (2024). Exploring core knowledge in interdisciplinary research: insights from topic modeling analysis. Applied Sciences, 14(21), 10054. https://doi.org/10.3390/app142110054.
Xie, S., & Liu, Z. (2025). Library resource recommendation integrating collaborative filtering and association rules. Journal of Computational Methods in Sciences and Engineering. https://doi.org/10.1177/14727978251364442.
Yang, K., Tian, C., Xian, H., Tian, W., & Zhang, Y. (2023). Query on the cloud: improved privacy-preserving k-nearest neighbor classification over the outsourced database. World Wide Web, 26(4), 1747–1774. https://doi.org/10.1007/s11280-022-01093-4 .
Zhang, W., Ji, M., Wang, H., Zhang, C., Xu, S., Wu, Y., & Song, F. (2020). An exploration of how the resourcing of staff and book purchasing affects the development of China’s public school libraries in Guangzhou. Journal of the Australian Library and Information Association, 69(1), 70–92. https://doi.org/10.1080/24750158.2019.1702140
Zhang, X., & Zhang, J. (2023). Analysis and research on library user behavior based on apriori algorithm. Measurement: Sensors, 27, 100802. https://doi.org/10.1016/j.measen.2023.100802.
Zhang, X., Zang, L., Bai, W., & Liu, H. (2024). Big data analysis and application of library information resources. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns-2024-1212.
Zhou, X. (2022). Personalized book recommendation of the university library coupled with collaborative filtering and FP-Growth algorithm. Proceedings of the 4th International Conference on Machine Learning and Machine Intelligence, Hangzhou, China, September 17-19, 2021 (pp. 172–176). ACM Digital Library. https://doi.org/10.1145/3490725.3490751.
Zhu, Q., & Xie, X. (2022). Users’ needs and expectations of immersive learning spaces in an academic library: A survey. Journal of Librarianship and Information Science, 55(2), 371–382. https://doi.org/10.1177/09610006221081844.
Zhou, Y. (2020). Design and implementation of book recommendation management system based on improved Apriori algorithm. Intelligent Information Management, 12(3), 75–87. https://doi.org/10.4236/iim.2020.123006.
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