Using the Association Rule to Analyze the Book Borrowing Behavior of Prince of Songkla University Students with Data Mining Techniques
DOI:
https://doi.org/10.14456/jiskku.2025.16Keywords:
Association rule, ฺฺBook, Borrowing behavior, Data mining, FP-Growth algorithmAbstract
Purpose: To analyze the book borrowing behavior of undergraduate students at Prince of Songkla University using association rules and data mining techniques.
Methodology: This research is quantitative research using data mining techniques to analyze the book borrowing behavior of undergraduate students from eight faculties, using data extracted from the automated library system database of the John F. Kennedy Library, Office of Academic Resources, Prince of Songkla University, Pattani campus. The dataset covers the academic years 2021 to 2024 (June 21, 2021 - March 20, 2025). The data were analyzed and presented using frequency, percentages, and association rules generated through the FP-Growth algorithm with a minimum support at 0.3 and a minimum confidence at 0.7.
Finding: 1) Students borrowed a total of 40,581 items. The Faculty of Education had the highest number of borrowings, with 10,246 items (25.25%). The most borrowed classification was [600] Applied Sciences, accounting for 9,322 items (22.97%), and 2) The association rules of students’ borrowing behavior varied across faculties, and the resulting rules showed that the content of borrowed books (by classification section) corresponded with the students' respective fields of study.
Application of this study: The research results can assist librarians or library staff by providing useful insights for recommending books that align with students' borrowing behaviors. Furthermore, the findings can support more accurate collection development based on the user needs and promote more effective and efficient use of library resources.
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