Many approaches to decision support for the academic library acquisition budget allocation have been proposed to diversely reflect the management requirements. Different from these methods that focus mainly on either statistical analysis or goal programming, this paper introduces a model (ABAMDM, acquisition budget allocation model via data mining) that addresses the use of descriptive knowledge discovered in the historical circulation data explicitly to support allocating library acquisition budget. The major concern in this study is that the budget allocation should be able to reflect a requirement that the more a department makes use of its acquired materials in the present academic year, the more it can get budget for the coming year. The primary output of the ABAMDM used to derive weights of acquisition budget allocation contains two parts. One is the descriptive knowledge via utilization concentration and the other is the suitability via utilization connection for departments concerned. An application to the library of Kun Shan University of Technology was described to demonstrate the introduced ABAMDM in practice.
All Science Journal Classification (ASJC) codes
- Information Systems
- Media Technology
- Computer Science Applications
- Management Science and Operations Research
- Library and Information Sciences