Abstract
With the advent of the era of big data, massive data make the informationization work of academic libraries severely challenging in all aspects. The application of data mining technology to the various services of academic libraries to cope with this challenge has gradually become the mainstream trend. In order to better detect the overall trends and research hot spots of data mining in Chinese academic libraries, 329 related studies were extracted from CNKI (China National Knowledge Infrastructure) and analyzed by using bibliometrics and keyword network analyses. Bibliometrics was used to analyze the distribution of the overall trends, authors, institutions, research levels, and keywords in the related field. Cluster analysis was applied to the keyword co-occurrence network to show hot issues in the application research of data mining in Chinese academic libraries. Results indicate that findings in this area generally show an upward trend, but few high-yielding authors are found, the volume of journals is low, research institutions are scattered, and the rate of funding support is not high. At the method level, association rules and cluster analysis are hot data mining technologies in Chinese academic library research. At the application level, the fields related to personalized services and big data continue to be hot research areas.
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References
Agrawal, R.: Data mining: Crossing the chasm. ReasearchGate. In: Invited Talk at the 5th ACM SIGKDD international conference on knowledge discovery and data mining (KDD ’99), San Diego, California (1999)
Baeta, G., Soltes, M., Kovac, V.: Application of cluster analysis in the process of competitiveness modelling of slovak republic regions. Trans. Business Econ. 16(3), 129–147 (2017)
Castillo-Zuniga, L.-R., Munoz-Arteaga, J.: Combination of techniques of big data analytics and semantic web for the detection of vocabulary of harassment school in internet. DYNA 92(2), 141–142 (2017)
Fagan, J.C.: The effects of reference, instruction, database searches, and ongoing expenditures on full-text article requests: An exploratory analysis. J. Acad. Librariansh. 40(3–4), 264–274 (2014). https://doi.org/10.1016/j.acalib.2014.04.002
Hájek, P., Stejskal, J.: Library user behavior analysis-Use in economics and management. WSEAS Trans. Bus. Econ. 11, 107–116 (2014)
Han, J., Kamber, M., Pei, J.: Data mining: Concepts and techniques, 3rd edn. Elsevier, New York (2011)
Huang, P., Lin, F., Xu, L. J., Kang, Z. L., Zhou, J. L., Yu, J.S.: Improved ACO-based sweep coverage scheme considering data delivery. Int J Simul. Model. 16(2), 289–301 (2017)
Julijana, A: Equity Market Integration is Driven by the Advances in Information and Communications Technology. Trans. Business Econ. 15(1), 115–126 (2016)
Leonard, M.F., Haas, S.C., Kisling, V.N.: Metrics and science monograph collections at the Marston Science Library. Issues in science and technology librarianship62. University of Florida. http://dialnet.unirioja.es/servlet/articulo?codigo=3706724. Accessed 26 January 2018 (2018)
Li, G., Song, G., Xiaoxin, T., Hongbing, C., Qiuhong, T.: Design and implementation of new books noting personalized recommendation system based on circulation logs. New Techn. Libr. Inf. Service 6, 89–93 (2012)
Oprea, D.: Big questions on big data. Revista de Cercetare si Interventie Sociala. 55, 111–126 (2016)
Sewell, R.R.: Who is following us? Data mining a library’s Twitter followers. Libr. Hi Tech 31(1), 160–170 (2013). https://doi.org/10.1108/07378831311303994
Siguenza-Guzman, L., Saquicela, V., Avila-Ordóñez, E., Vandewalle, J., Cattrysse, D.: Literature review of data mining applications in academic libraries. J. Acad. Librariansh. 41, 499–510 (2015). https://doi.org/10.1016/j.acalib.2015.06.007
Siguenza-Guzman, L., Van Den Abbeele, A., Vandewalle, J., Verhaaren, H., Cattrysse, D.: A holistic approach to supporting academic libraries in resource allocation processes. The Library Quarterly: Information, Community, Policy 85(3), 295–318 (2015)
Tang, X.: Using SMImport control to achieve the data import development of data mining. The Libr. J. Shandong 4, 83–88 (2011)
Tang, Q., Hongbing, C., Xiaoxin, T., Li, G., Song, G.: A research of personalized topic recommendation service in university libraries. Res. Libr. Sci. 13, 53–58 (2012)
Tang, X., Li, G., Qiuhong, T., Hongbing, C., Song, G.: Design and implementation of personalized e-book purchasing recommendation system in university libraries. New Techn. Library Inf. Service 3, 83–88 (2012)
Tempelman-Kluit, N., Pearce, A.: Invoking the user from data to design. Coll. Res. Libr. 75(5), 616–640 (2014). https://doi.org/10.5860/crl.75.5.616
Tianji, Z.D.: The significance of web log database data mining to interview decision. Libr. J. 11, 41–42 (2011)
Todorinova, L., Huse, A., Lewis, B., Torrence, M.: Making decisions: using electronic data collection to re-envision reference services at the USF Tampa Libraries. Public Services Quarterly 7(1–2), 34–48 (2011). https://doi.org/10.1080/15228959.2011.572780
Uthurusamy, R.: From data mining to knowledge discovery: Current challenges and future directions. FAYGAD U. Advances in Knowledge Discovery and Data Mining, pp 561–569. The MIT Press, Cambridge (1996)
Wu, X., Bingxiang, L.: Application of cluster analysis to the university library management. Comput. Dev. Appl. 9, 15–16 (2011)
Wu, X., Bingxiang, L.: Application of association rules to the university library management. Modern Comput. 7, 49–52 (2011)
Xudong, W.U., Bingxiang, L., Xiewei, W.: Application of data mining to the university library. Neijiang Sci Techn. 4, 128 (2011)
Yonglin, X., Xudong, W.U., Bingxiang, L.: Data mining application in personalized service of university library. Sci. Mosaic. 10, 58–62 (2012)
Yang, S. -T.: An active recommendation approach to improve book-acquisition process. Int. J. Elect. Business Manag. 10(2), 163–173 (2012)
Yi, Z.: Planning change in the information age: approaches of academic library directors in the United States. Int J Knowl Culture Change Manag. 10(12), 155–176 (2011)
Yi, Z.: Conducting meetings in the change process: approaches of academic library directors in the United States. Libr. Manag. 33(1/2), 22–35 (2012). https://doi.org/10.1108/01435121211203293
Yu, J.: Overview of the data mining application research of domestic library. Libr. Inf. 2, 137–141 (2015)
Zhang, Q.S., Wang, X.Y.: Research of personalized information service based on association rules. Advanced Materials Research 760-762, 1800–1803 (2013). https://doi.org/10.4028/www.scientific.net/AMR.760-762.1800
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Huancheng, L., Tingting, W. & Rocha, Á. An Analysis of Research Trends on Data Mining in Chinese Academic Libraries. J Grid Computing 17, 591–601 (2019). https://doi.org/10.1007/s10723-018-9461-3
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DOI: https://doi.org/10.1007/s10723-018-9461-3