Knowledge Discovery from Library Automation via Bibliomining using the Apriori Algorithm

Abstract

Today which is called as the digital age with the considerably developing information systems, the constant increase in the data amount being recorded has revealed the concept of big data. Obtaining the strategic information which is crucial for decision-makers especially in managerial terms is only possible through processing these big data with accurate techniques. Data mining techniques have frequently been used in recent years in order to reach meaningful and useful knowledge among data stacks. In this study, the Apriori Algorithm was used for the managers of university library information systems, which provide data-oriented service, to make investment decisions in the future effectively and create user profiles. Within the scope of the study, an application was performed on the basis of an information system comprising of real data of Erzincan Binali Yıldırım University Central Library. By means of association rules, which are one of the descriptive models of data mining, ten different association rules regarding the joint borrowing of publications were applied and results were obtained in the confidence intervals of 57.1% and 95.8%. In addition, information such as library inventory, member profile, and publication borrowing habits were obtained and evaluations were made in line with this information at the end of the study.