Literature Review of Data Mining Applications in Academic Libraries

This article provides a comprehensive literature review and classification method for data mining techniques applied to academic libraries. To achieve this, forty-one practical contributions over the period 1998-2014 were identified and reviewed for their direct relevance. Each article was categorized according to the main data mining functions: clustering, association, classification, and regression; and their application in the four main library aspects: services, quality, collection, and usage behavior. Findings indicate that both collection and usage behavior analyses have received most of the research attention, especially related to collection development and usability of websites and online services respectively. Furthermore, classification and regression models are the two most commonly used data mining functions applied in library settings.Additionally, results indicate that the top 6 journals of articles published on the application of data mining techniques in academic libraries are: College and Research Libraries, Journal of Academic Librarianship, Information Processing and Management, Library Hi Tech, International Journal of Knowledge, Culture and Change Management, and The Electronic Library. Scopus is the multidisciplinary database that provides the best coverage of journal articles identified. To our knowledge, this study represents the first systematic, identifiable and comprehensive academic literature review of data mining techniques applied to academic libraries.

• Auteur principal: Sigüenza Guzmán, Lorena Catalina (author)
• Autres auteurs: Ávila Ordóñez, Elina María (author), Saquicela Galarza, Víctor Hugo (author)
• Format: article
• Publié: 2015
• Sujets: Academic Libraries; Bibliomining; Data Mining; Literature Review
• Accès en ligne: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937163361&doi=10.1016%2fj.acalib.2015.06.007&partnerID=40&md5=b66bc2ab11f619e14adfdbef9ed91fcb; http://dspace.ucuenca.edu.ec/handle/123456789/29072