Clustering Digital Resources for Collection Development and Improving Archive and Digital Library Services

Khademizadeh, Shahnaz; Tabatabai Amiri, Faeze Sadat; Nasiri, Mohammadreza

doi:10.22055/slis.2024.40034.1874

Clustering Digital Resources for Collection Development and Improving Archive and Digital Library Services

Document Type : Original Article

Authors

Shahnaz khademizadeh ¹

faeze sadat tabatabai amiri ²

mohammadreza nasiri ³

¹ Associate Professor, Department of Information Science, Faculty of Education and Psychology, Shahid Chamran University, Ahvaz, Iran.

² PhD student, Department of Information Science, Shahid Chamran University, Ahvaz, Iran.

³ Graduate of Master, Department of Information Science, Qom University, Iran.

https://doi.org/10.22055/slis.2024.40034.1874

Abstract

Background and Objectives: Digital libraries generate large volumes of transaction and search-log data that can reveal users’ actual information needs and support evidence-based collection development, digitization priorities, resource organization, and personalized services. The Ayatollah Boroujerdi Digital Library in Qom holds distinctive collections, including manuscripts, hot-metal and lithographic publications, textbooks, theses, and other rare resources. Despite the value of these collections, conventional usage statistics provide limited insight into relationships among the subjects searched by users. This study therefore aimed to identify clusters of subject use and association patterns among users’ searches in the library’s digital databases. More specifically, it examined which subjects were frequently used together and how the resulting patterns could contribute to acquisition, collection development, further digitization, improved service quality, and the future design of recommender systems.
Methodology: This cross-sectional analytical study examined ten years of digital-library usage data covering the period 2011–2021. The dataset was extracted from the NOSA digital-resource archive system. The original databases were stored in SQL format and the extracted records were converted to CSV files for analysis. Data preparation and cleaning included separating date and time values recorded in a single field, converting ordered subject labels into codes, and removing records associated with IP addresses generating more than 50 searches. This procedure was intended to distinguish searches by individual users from activity originating from shared systems or internal library computers. Subjects assigned to each resource were retrieved through resource codes and consolidated into principal subject categories. A two-step clustering procedure was applied because it is suitable for large datasets and can accommodate both categorical and quantitative data. Association-rule mining using the Apriori algorithm was then employed to identify dependencies and co-occurrence patterns among searched subjects. IBM Modeler version 18 was used for clustering and association-rule analysis. For network visualization, the data were transformed into a matrix and analyzed with the bibliometrix package in R. The clustering analysis concerned searched subjects rather than the physical type or digital format of resources.
Findings: PDF was the dominant format, accounting for 98.35% of the resources used. Manuscripts represented 99.91% of the records for which resource type was identifiable, demonstrating the central role of this collection in digital-library use. According to the recorded counts, 709,337 of the 1,048,575 visits, approximately 68%, were made by guest users, whereas 339,238 were associated with non-guest users. Overall use increased markedly over the study period, reaching 245,187 recorded uses in 2018 and 244,333 in 2020. December, April, and January had the highest aggregated use, and the busiest daily period was between 10:00 a.m. and 12:00 noon. Two subject clusters were identified. Cluster 1 covered approximately 56% of the data and was characterized by jurisprudence, principles of jurisprudence, prayer, literature, medicine, mysticism, history, ethics, and sciences. Cluster 2 accounted for approximately 44% and included theology, Hadith, syntax, philosophy, logic, poetry, astronomy, prayers, interpretation, and miscellaneous subjects. Network analysis showed that jurisprudence, syntax, theology, principles of jurisprudence, and Hadith were among the most prominent and highly connected subjects. Jurisprudence had particularly strong co-search relationships with logic, ethics, medicine, Hadith, history, and syntax. The association rules showed confidence values of approximately 55% for some relationships involving jurisprudence, most notably its relationships with logic, ethics, and medicine. By contrast, subjects such as divination, elegies concerning martyrdom, commentary, and arithmetic attracted comparatively little attention.
Discussion: The findings demonstrate that digital-library logs can provide managers with evidence of actual user behavior that is not readily available through surveys or aggregate visit statistics. The predominance of manuscripts and the concentration of use in the first subject cluster suggest that digitization, description, and collection-development activities should give particular attention to jurisprudence and its related fields. At the same time, the relatively limited use of the library’s rare hot-metal and lithographic holdings indicates a need for better online promotion, standardized organization, and improved discoverability. Because a large proportion of users access the library as guests, encouraging optional membership could make it possible to analyze longitudinal interests and provide more personalized services without restricting public access. The co-search patterns can also inform subject navigation, “related resources” features, and recommender systems. Nevertheless, the results should be interpreted as associations rather than causal relationships. They reflect one library, depend on the quality of subject coding and log data, and do not incorporate resource format into the clustering model. Future research should compare Apriori with techniques such as k-means and test combined clustering and association-rule approaches in other digital-library environments.

Keywords

Clustering

Collection development.Digital library

Data mining

Ayatollah Boroujerdi library

Subjects

English Issue

Ahmad, P., Brogan, M., & Johnstone, M.N. (2014). The e-book power user in academic and research libraries: Deep log analysis and user customisation. Australian Academic and Research Libraries, 45 (1): 35–47. http://dx.doi.org/10.1080/00048623.2014.885374

Bussaban, K. and Kularbphettong, K. (2014). Analysis of users’ behavior on book loan log based on association rule mining. World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 8 (1): 18-20.

Chen, A. P. and Chen, C. C. (2006). A new efficient approach for data clustering in electronic library using ant colony clustering algorithm, The Electronic Library, 24 (4): 548-559.

Chen, C. C. and Chen, A. P. (2007). Using data mining technology to provide a recommendation service in the digital library, The Electronic Library, 25 (6): 711-724.

Chen, R. S., Tsai, Y. S., Yeh, K., Yu, D. and Bak-Sau, Y. (2008). Using data mining to provide recommendation service, Wseas Transactions on Information Science and Applications, 5 (4): 459-474.

Decker, R., & Hermelbracht, A. (2006). Planning and evaluation of new academic library services by means of Web-based conjoint analysis. The Journal of Academic Librarianship, 32 (6): 558–572. http://dx.doi.org/10.1016/j.acalib.2006.06.016.

Decker, R., & Höppner, M. (2006). Strategic planning and customer intelligence in academic libraries. Library Hi Tech, 24 (4): 504–514. http://dx.doi.org/10.1108/07378830610715374.

Emmons, M., & Wilkinson, F.C. (2011). The academic library impact on student persistence. College and Research Libraries, 72 (2): 128–149.

Finnell, J., & Fontane, W. (2010). Reference question data mining: A systematic approach to library outreach. Reference and User Services Quarterly, 49 (3): 278–286.

Hájek, P., & Stejskal, J. (2014). Library user behavior analysis- Use in economics and management. WSEAS Transactions on Business and Economics, 11: 107–116.

Jin, W. and Sheng, H. (2017). Research on management of libraries in universities and colleges based on k-means clustering algorithm under big data environment, Revista de la Facultad de Ingeniería, 32 (8): 177-181.

Jomsri, P. (2014). Book recommendation system for digital library based on user proﬁles by using association rule, Innovative computing technology (INTECH), Fourth International Conference on the Innovative Computing Technology (INTECH), IEEE, Luton: 130-134.

Kao, S. C.; Chang, H. C.; & Lin, C. H. (2003). Decision support for the academic library acquisition budget allocation via circulation database mining, Information Processing and Management, 39 (1): 133–147. Doi:10.1016/S0306-4573(02)00019-5.

King, J. D., Li, Y., Tao, X., & Nayak, R. (2007). Mining world knowledge for analysis of search engine content. Web Intelligence and Agent Systems, 5 (3): 233–253.

Koulouris, A., & Kapidakis, S. (2012). Policy route map for academic libraries' digital content. Journal of Librarianship and Information Science, 44 (3): 163–173. http://dx.doi.org/10.1177/0961000612444299.

Kovacevic, A.; Devedzic, V.; & Pocajt, V. (2010). Using data mining to improve digital library services, The Electronic Library, 28 (6): 829 – 843, Doi: 10.1108/02640471011093525

Krishnamurthy, V.; and Balasubramani, R. (2014). An association rule mining approach for libraries to analyses user interest, Intelligent computing applications (ICICA), International Conference on Intelligent Computing Applications, IEEE, Coimbatore: 122-125.

Li, J. and Chen, P. (2008). The application of association rule in library system, Knowledge Acquisition and Modeling Workshop. International Symposium on Knowledge Acquisition and Modeling Workshop, IEEE, Wuhan: 248-251.

Li, X. (2014). An algorithm for mining frequent itemsets from library big data. Journal of Software, 9 (9): 2361–2365. http://dx.doi.org/10.4304/jsw.9.9.2361-2365.

Li, y. (2017), Research on application of BP neural network in library electronic resource quality evaluation, Revista de la Facultad de Ingeniería, 32 (5): 605-613.

Liu, y. (2018), Data mining of university library management based on improved collaborative ﬁltering association rules algorithm, Wireless Personal Communications, 102 (4): 3781-3790.

Long, X. and Wu, Y. (2012), Borrowing data mining based on association rules, International Conference on Computer Science and Electronics Engineering (ICCSEE), IEEE, Hangzhou, 2: 239-242.

Mishra, R.N. and Mishra, A. (2013). Relevance of data mining in digital library. International Journal of Future Computer and Communication, 2 (1): 10-14.

Nicholas, D., Huntington, P., Monopoli, M., & Watkinson, A. (2006). Engaging with scholarly digital libraries (publisher platforms): The extent to which “added-value” functions are used. Information Processing and Management, 42 (3): 826–842. http://dx.doi.org/10.1016/j.ipm.2005.03.019.

Nicholson, S. (2003). The bibliomining process: Data warehousing and data mining for library decision-making. Information Technology and Libraries, 22 (4), 146–151.

Ochilbek, R. (2019), Using data mining techniques to predict and detect important features for book borrowing rate in academic libraries, 15th International Conference on Electronics, Computer and Computation (ICECCO), IEEE: 1-5.

Papavlasopoulos, S., & Poulos, M. (2012). Neural network design and evaluation for classifying library indicators using personal opinion of expert. Library Management, 33 (4/5): 261–271. http://dx.doi.org/10.1108/01435121211242308.

Prehanto, D.R., Indriyanti, A.D., Permadi, G.S., Vitadiar, T.Z. and Jayanti, F.D. (2020), Library book modeling data using the association rule method with Apriori algorithm in determining book placement and analysis of book loans, International Journal of Advanced Science and Technology, 29 (5): 1244-1250.

Pu, H., & Yang, C. (2003). Enriching user‐oriented class associations for library classiﬁcation schemes. The Electronic Library, 21 (2): 130–141. http://dx.doi.org/10.1108/02640470310470507.

Renaud, J., Britton, S., Wang, D. and Ogihara, M. (2015), Mining library and university data to understand library use patterns, The Electronic Library, 33 (3): 355-372.

Roy, D., Ding, C., Jin, I. and Thomas, D. (2018). Association rule based clustering of electronic resources in university digital library, International conference on theory and practice of digital libraries, 349-353.

Ruixiang, O. (2018), The purchasing management and practice of sci-tech books in university library based on back-propagation artiﬁcial neural network, International workshop on advances in social sciences (IWASS), Hong Kong: 714-719.

Samson, S. (2014). Usage of e-resources: Virtual value of demographics. The Journal of Academic Librarianship, 40 (6): 620–625. http://dx.doi.org/10.1016/j.acalib.2014.10.005.

Sewell, Robin R. (2013). Who is following us? Data mining a library's Twitter followers. Library Hi Tech, 31 (1): 160–170. http://dx.doi.org/10.1108/07378831311303994.

Shieh, J. C. (2012). From website log to ﬁndability. The Electronic Library, 30 (5): 707–720. http://dx.doi.org/10.1108/02640471211275747.

Siguenza-Guzman, L.; Van Den Abbeele, A.; Vandewalle, J.; Verhaaren, H.; & Cattrysse, D. (2015). A holistic approach to supporting academic libraries in resource allocation processes. The Library Quarterly: Information, Community, Policy, 85 (3): 295–318.

Silwattananusarn, T.; kulkanjanapiban, P. (2020). Mining and Analyzing Patron’s Book-Loan Data and University Data to Understand Library Use Patterns. International Journal of Information Science and Management (IJISM), 18 (2): 151-172.

Siriprasoetsin, P., Tuamsuk, K., & Vongprasert, C. (2011). Factors affecting customer relationship management practices in Thai academic libraries. The International Information and Library Review, 43 (4): 221–229. http://dx.doi.org/10.1016/j.iilr.2011.10.008.

Tempelman-Kluit, N., & Pearce, A. (2014). Invoking the user from data to design. College and Research Libraries, 75 (5): 616–640. http://dx.doi.org/10.5860/crl.75.5.616.

Todorinova, L., Huse, A., Lewis, B., & Torrence, M. (2011). Making decisions: Using electronic data collection to re-envision reference services at the USF Tampa Libraries. Public Services Quarterly, 7 (1–2): 34–48. http://dx.doi.org/10.1080/15228959.2011.572780.

Tosaka, Y., & Weng, C. (2011). Reexamining content-enriched access: Its effect on usage and discovery. College and Research Libraries, 72 (5): 412–427. http://dx.doi.org/10.5860/crl-137.

Yang, S. T. (2012). An active recommendation approach to improve book-acquisition process. International Journal of Electronic Business Management, 10 (2): 163–173.

Yi, C.; Xia, Y.; and Zhang, Z.Y. (2014). Study the personal push service of university library based on big data mining, Advanced Materials Research, 998/999: 1261-1265.

Yi, Z. (2011). Planning change in the information age: Approaches of academic library directors in the United States. International Journal of Knowledge, Culture and Change Management, 10 (12): 155–176.

Yi, Z. (2012). Conducting meetings in the change process: Approaches of academic library directors in the United States. Library Management, 33 (1/2): 22–35. http://dx.doi.org/10.1108/01435121211203293.

Yi, K., Chen, T. and Cong, G. (2018). Library personalized recommendation service method based on improved association rules. Library Hi Tech, 36 (3): 443-457.

Yu, P. (2011). Data mining in library reader management, International conference on network computing and information security, IEEE, Guilins, 2: 54-57.

Wang, P. (2014), Research on the prediction method for library reader ﬂow based on evolutionary neural network. International Conference on Vehicle and Mechanical Engineering and Information Technology (VMEIT), Applied mechanics and materials, Beijing, 543/547: 2128-2132.

Weiner, S. (2009). The contribution of the library to the reputation of a university. The Journal of Academic Librarianship, 35 (1): 3–13. http://dx.doi.org/10.1016/j.acalib.2008.10.003.

Will, N. (2006). Data-mining: Improvement of university library services. Technological Forecasting and Social Change, 73 (8): 1045–1050. http://dx.doi.org/10.1016/j.techfore.2006.05.006.

Wu, C. H. (2003). Data mining applied to material acquisition budget allocation for libraries: Design and development. Expert Systems with Applications, 25 (3): 401–411. http://dx.doi.org/10.1016/S0957-4174(03)00065-4.

Zhang, C. (2014). Research on construction of universities sports management information system based on data mining, Applied Mechanics and Materials, 687/691:2702-2705.

Zhang, Q. S., & Wang, X.Y. (2013). Research of personalized information service based on association rules. Advanced Materials Research, 760-762: 1800–1803. http://dx.doi.org/10.4028/www.scientiﬁc.net/AMR.760-62.1800.

Zhang, Q. and Wang, P. (2016). A study on readers’ satisfaction of university library based on bp neural network. Journal of Computational and Theoretical Nanoscience, 3 (9): 6005-6010.

Zhou, Y. (2020), Design and implementation of book recommendation management system based on improved Apriori algorithm. Intelligent Information Management, 12 (3): 75-87.

Zweibel, S., & Lane, Z. B. (2012). Probing the effects of policy changes by evaluating circulation activity data at Columbia University Libraries. The Serials Librarian, 63 (1): 17–27. http://dx.doi.org/10.1080/0361526X.2012.687850.