Abstract

Association rule mining is a technique for discovering patterns, associations, and relationships in large data sets or in a variety of databases such as relational, transactional, and other archives or repositories. It is significantly used in libraries to provide a data-driven approach in management of books, reports, theses, manuscripts, and other literature. This article was conducted to examine book borrowing patterns using the Apriori algorithm for efficient book shelving to assist Laguna State Polytechnic University’s library in effectively managing resources, and services. The three year book borrower records of Laguna State Polytechnic University were used as the dataset in this article. Hence, rapidminer was used as a data mining tool in implementing apriori algorithm in the latter and for association discovery. Through the use of apriori algorithm, it was discovered that histories, and consumer preferences books give a high relationship rating therefore, the library may consider rearranging the shelves and place the latter closer with each other. Moreover, all the combinations of two item sets or books with a confidence value greater than 60% as shown in this article were strongly advised to be placed or grouped together for a more effective shelving and efficient searching of books.