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http://dx.doi.org/10.3743/KOSIM.2018.35.1.013

Subject Association Analysis of Big Data Studies: Using Co-citation Networks  

Kwak, Chul-Wan (강남대학교 산업데이터사이언스학부)
Publication Information
Journal of the Korean Society for information Management / v.35, no.1, 2018 , pp. 13-32 More about this Journal
Abstract
The purpose of this study is to analyze the association among the subject areas of big data research papers. The subject group of the units of analysis was extracted by applying co-citation networks, and the rules of association were analyzed using Apriori algorithm of R program, and visualized using the arulesViz package of R program. As a result of the study, 22 subject areas were extracted and these subjects were divided into three clusters. As a result of analyzing the association type of the subject, it was classified into 'professional type', 'general type', 'expanded type' depending on the complexity of association. The professional type included library and information science and journalism. The general type included politics & diplomacy, trade, and tourism. The expanded types included other humanities, general social sciences, and general tourism. This association networks show a tendency to cite other subject areas that are relevant when citing a subject field, and the library should consider services that use the association for academic information services.
Keywords
big data; association analysis; co-citation networks; R program; academic information services;
Citations & Related Records
Times Cited By KSCI : 10  (Citation Analysis)
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