An Analysis of Research Trends on Data Mining in Chinese Academic Libraries

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.

[1]  Gao Song,et al.  Design and Implementation of Personalized E-book Purchasing Recommendation System in University Libraries , 2012 .

[2]  Barbara Lewis,et al.  Making Decisions: Using Electronic Data Collection to Re-Envision Reference Services at the USF Tampa Libraries , 2011 .

[3]  Li Gaohu,et al.  Design and Implementation of New Books Noting Personalized Recommendation System Based on Circulation Logs , 2012 .

[4]  Ramasamy Uthurusamy,et al.  From Data Mining to Knowledge Discovery: Current Challenges and Future Directions , 1996, Advances in Knowledge Discovery and Data Mining.

[5]  Dumitru Oprean.  BIG QUESTIONS ON BIG DATA , 2016 .

[6]  Jody Condit Fagan.  The Effects of Reference, Instruction, Database Searches, and Ongoing Expenditures on Full-text Article Requests: An Exploratory Analysis , 2014 .

[7]  Zhixian Yi.  Conducting meetings in the change process , 2011 .

[8]  Francisco Javier Luna Rosas,et al.  COMBINACIÓN DE TÉCNICAS DE BIG DATA ANALYTICS Y WEB SEMÁNTICA PARA LA DETECCIÓN DE VOCABULARIO DE ACOSO ESCOLAR EN INTERNET , 2017 .

[9]  P. Hájek,et al.  Library User Behavior Analysis – Use in Economics and Management , 2014 .

[10]  Joos Vandewalle,et al.  A Holistic Approach to Supporting Academic Libraries in Resource Allocation Processes , 2015, The Library Quarterly.

[11]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[12]  D. Madigan,et al.  Proceedings : KDD-99 : the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 15-18, 1999, San Diego, California, USA , 1999 .

[13]  Shih-Ting Yang.  An Active Recommendation Approach to Improve Book-Acquisition Process , 2012, Int. J. Electron. Bus. Manag..

[14]  Robin R. Sewell.  Who is following us? Data mining a library's Twitter followers , 2013, Libr. Hi Tech.

[15]  Stephanie C. Haas,et al.  Metrics and Science Monograph Collections at the Marston Science Library, University of Florida , 2010 .

[16]  Jian Pei,et al.  Data Mining: Concepts and Techniques, 3rd edition , 2006 .

[17]  Qing Song Zhang,et al.  Research of Personalized Information Service Based on Association Rules , 2013 .

[18]  Wang Hui-mi.  Application of data mining in university library , 2014 .

[19]  Z. L. Kang,et al.  Improved ACO-Based Sweep Coverage Scheme Considering Data Delivery , 2017 .

[20]  Dirk Cattrysse,et al.  Literature Review of Data Mining Applications in Academic Libraries , 2015 .

[21]  Nadaleen Tempelman-Kluit,et al.  Invoking the User from Data to Design , 2014, Coll. Res. Libr..

[22]  Yang Song-tao.  Application of Association Rules to the University Library Management , 2011 .