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Information Processing & Management
Volume 39, Issue 1, January 2003, Pages 133-147
 
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doi:10.1016/S0306-4573(02)00019-5    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2002 Elsevier Science Ltd. All rights reserved.

Decision support for the academic library acquisition budget allocation via circulation database mining

S. -C. KaoE-mail The Corresponding Author, a, H. -C. Changb and C. -H. Linc

a Department of Information Management, Kun Shan University of Technology, 949 Da Wan Road, Yung Kung, Tainan 710, Taiwan, ROC b Department of Business Administration, National Cheng Kung University, 1 University Road, Tainan 710, Taiwan, ROC c Department of Industrial Management Science, National Cheng Kung University, 1 University Road, Tainan 710, Taiwan, ROC

Received 21 September 2001; 
accepted 13 December 2001. ;
Available online 3 February 2002.

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Abstract

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.

Author Keywords: Decision support; Acquisition budget allocation; Data mining

Article Outline

1. Introduction
2. The ABAMDM
2.1. The architecture of ABAMDM
2.1.1. Preprocess of circulation data
2.1.2. Generation of decisional knowledge
2.2. An illustrated example
3. An application case
3.1. Application characteristics
3.2. The results and findings
4. Concluding remarks
References








 
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