Mining and Analyzing Patron’s Book-Loan Data and University Data to Understand Library Use Patterns

• Main Authors: Tipawan Silwattananusarn, Pachisa Kulkanjanapiban
• Format: Article
• Language: English
• Published: Regional Information Center for Science and Technology (RICeST) 2020-08-01
• Series: International Journal of Information Science and Management
• Subjects: data mining; association rules; cluster analysis; knowledge dependency; data analytics; information management
• Online Access: https://ijism.ricest.ac.ir/index.php/ijism/article/view/1803
• ISSN: 2008-8302; 2008-8310

<p>The purpose of this paper is to study the patron’s usage behavior in an academic library. This study investigates on pattern of patron’s books borrowing in Khunying Long Athakravisunthorn Learning Resources Center, Prince of Songkla University that influence patron’s academic achievement during on academic year 2015-2018. The study collected and analyzed data from the libraries, registrar, and human resources. The students’ performance data was obtained from PSU Student Information System and the rest from ALIST library information system. WEKA was used as the data mining tool employing data mining techniques of association rules and clustering. All data sets were mined and analyzed to identify characteristics of the patron’s book borrowing, to discover the association rules of patron’s interest, and to analyze the relationships between academic library use and undergraduate students’ achievement. The results reveal patterns of patron’s book loan behavior, patterns of book usage, patterns of interest rules with respect to patron’s interest in book borrowing, and patterns of relationships between patron’s borrowing and their grade. The ability to clearly identify and describe library patron’s behavior pattern can help library in managing resources and services more effectively. This study provides a sample model as guideline or campus partnerships and for future collaborations that will take advantage of the academic library information and data mining to improve library management and library services.</p>