Association rules mining for knowledge management: a case study of library services

Authors:
Chu Chai Henry Chan;Ming-Hsiu Lee;Yun-Chiang Kwang
Affiliations:
E-Business Research Lab, Department of Industrial Engineering and Management, Chaoyang University of Technology, Wufong, Taichung, Taiwan, R.O.C.;E-Business Research Lab, Department of Industrial Engineering and Management, Chaoyang University of Technology, Wufong, Taichung, Taiwan, R.O.C.;E-Business Research Lab, Department of Industrial Engineering and Management, Chaoyang University of Technology, Wufong, Taichung, Taiwan, R.O.C.
Venue:
MMACTEE'07 Proceedings of the 9th WSEAS international conference on Mathematical methods and computational techniques in electrical engineering
Year:
2007

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Abstract

Data mining has been applied successfully in a lot of business communities for understanding and tracking behavior of individual or certain groups. To realize the actual needs of college students, this study proposes using data mining to discover the association rules of a library database. This major advantage of the study is to provide a novel mechanism by using problem-solving oriented approach rather than technical concept done by most of previous researches. We apply the Apriori algorithm as the core methodology of implementing association rules mining. To prove the proposed methodology, an empirical case study is conducted to find the association between different users' demands. Moreover, for knowing about students' preference, this work finds the association rules and searches the top ten ranking of books for students of three different colleges. One interesting finding is that different college students have different needs and behavior patterns. This conclusion can give a guideline for the studied library to understand the needs of different background students. Following the finding, the studied university can offer students suitable services in the future.