Data mining
Data mining and KDD: promise and challenges
Future Generation Computer Systems - Special double issue on data mining
Mining generalized association rules
Future Generation Computer Systems - Special double issue on data mining
ACM Transactions on Information Systems (TOIS)
An Extension to SQL for Mining Association Rules
Data Mining and Knowledge Discovery
Applications of Data Mining to Electronic Commerce
Data Mining and Knowledge Discovery
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Information Processing and Management: an International Journal
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Since the quality of a library is not in the number of materials that are available, but in the number of materials that are actually utilized, this is what a material acquisitions operation should be concerned with. In support of this goal, the library management has been paying increased attention to the value of the usage data in support of a variety of managerial decisions. Although many approaches and research reports have been extensively used to help library material acquisitions, the knowledge contained in circulation databases has hardly ever been used to investigate in-depth how the acquired materials are being used. Thus, there may not be adequate indications on which the material acquisitions operation can rely when making decisions. This paper introduces a model based on knowledge discovery (KDBMLMA) that embeds a circulation statistics mechanism and an association rule discovery mechanism to help derive the utilization of library material categories. A practical application case is presented and managerial implications discussed in this research.