Improving Book Lending Service in UTM Library with Apriori Rule-mining
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The use of data mining techniques to improve information system services is an ongoing research area that has gained immense attention. Academic libraries in institutions of higher learning are not left out in the quest to continuously support and provide meaningful services to their growing user base whose dependence on reliable services has increased exponentially. However, the proliferation of e-libraries, social media, commercial information providers and new publishing paradigms which seeks to provide free scholarly materials for reuse, revision, remix and redistribution challenge the aca...
The use of data mining techniques to improve information system services is an ongoing research area that has gained immense attention. Academic libraries in institutions of higher learning are not left out in the quest to continuously support and provide meaningful services to their growing user base whose dependence on reliable services has increased exponentially. However, the proliferation of e-libraries, social media, commercial information providers and new publishing paradigms which seeks to provide free scholarly materials for reuse, revision, remix and redistribution challenge the academic library's role as a pacesetter. This puts the latter under pressure to evolve and yield better ways of leading scholarship and maintaining patronage from everyone in the academic environment. Thus, this book reviews a set of factors influencing book lending among patrons and also discusses a data mining technique called association rule mining (ARM). It employs the use of Apriori ARM algorithm to build a recommender system which gives the academic libraries a leverage over its rivals as seen in the research findings and user acceptance testing results.
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