General Information
    • ISSN: 1793-8201 (Print), 2972-4511 (Online)
    • Abbreviated Title: Int. J. Comput. Theory Eng.
    • Frequency: Quarterly
    • DOI: 10.7763/IJCTE
    • Editor-in-Chief: Prof. Mehmet Sahinoglu
    • Associate Editor-in-Chief: Assoc. Prof. Alberto Arteta, Assoc. Prof. Engin Maşazade
    • Managing Editor: Ms. Mia Hu
    • Abstracting/Indexing: Scopus (Since 2022), INSPEC (IET), CNKI,  Google Scholar, EBSCO, etc.
    • Average Days from Submission to Acceptance: 192 days
    • APC: 800 USD
    • E-mail:
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Prof. Mehmet Sahinoglu
Computer Science Department, Troy University, USA
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.

IJCTE 2020 Vol.12(4): 97-101 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2020.V12.1271

Toward Understanding the User Behavior in Sports University Library Using Hierarchical Clustering

Yu-Chia Hsu, Yung-Che Li, and Yung-Hsuan Lin

Abstract—The library plays an important role in higher education. the emerging electronic media and digital contents bring libraries to encounter digital innovation and changes in leadership styles. However, analysis of the behavior of book borrowing is still a way to understand the demand of users, so as to provide sufficient resources and develop customized services. The purpose of this paper is to analyze the book borrowing data of the library in order to identify the user typologies. Numerous data records were collected from a sports university library in Taiwan. Each borrowing history record contains book detail and classification number. The characteristics of user behavior were described based on these data after cleaning, aggregation, and transforming. The hierarchical clustering techniques were applied to obtain the user typologies with similar behaviors. Five clusters, the general casual reader, athletes, Art and Literature lovers, course learner, and knowledge seeker, were obtained to represent the classic user typologies of a sports university.

Index Terms—Big data, data mining, behavioral patterns.

The authors are with the Department of Sports Information and Communication, National Taiwan University of Sport, Taichung, Taiwan (e-mail:


Cite:Yu-Chia Hsu, Yung-Che Li, and Yung-Hsuan Lin, "Toward Understanding the User Behavior in Sports University Library Using Hierarchical Clustering," International Journal of Computer Theory and Engineering vol. 12, no. 4, pp. 97-101, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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