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New Technology of Library and Information Service  2014, Vol. 30 Issue (10): 84-92    DOI: 10.11925/infotech.1003-3513.2014.10.13
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Application of Improved TFIDF Algorithm in Mining Potential Cooperation Relationship
Sun Hongfei, Hou Wei
School of Economics and Management, Northeast Dianli University, Jilin 132012, China
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[Objective] In order to remedy the defects of traditional methods in the mining potential cooperation relationship, improve the potential mining effect. [Methods] The paper proposes the improved TFIDF algorithm and applies to the potential cooperation relationship mining based on the analysis of the flaw and the insufficiency of simple calculation method, minimum value calculation method and the traditional TFIDF algorithm. [Results] The simple calculation method and the minimum value calculation method are greatly influenced by authors productivity, traditional TFIDF algorithm result is difficult to achieve the conversion from potential cooperation relationship for practical cooperation, and improved TFIDF algorithm shows very prominent based on regarding the applying research methods of information science field in 19 kinds of journals of Library and Information Science in "Chinese Core Journal of Peking University Directory (2012 Edition)" in recent 5 years as sample data. [Limitations] The improved TFIDF algorithm does not consider the influence between author ranking orders of potential cooperation. [Conclusions] The results show that the improved TFIDF algorithm is more scientific, has more advantages and better practical value than other traditional methods, through comparing and evaluating four data mining results.

Key wordsImproved TFIDF algorithm      Potential cooperation relationship      Data mining      Coupling analysis     
Received: 10 April 2014      Published: 28 November 2014
:  G350  

Cite this article:

Sun Hongfei, Hou Wei. Application of Improved TFIDF Algorithm in Mining Potential Cooperation Relationship. New Technology of Library and Information Service, 2014, 30(10): 84-92.

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[1] White H D, Griffith B C. Author Co-citation: A Literature Measure of Intellectual Structure [J]. Journal of the American Society for Information Science, 1981, 32(3): 163-171.
[2] Jarneving B. A Variation of the Calculation of the First Author Cocitation Strength in Author Cocitation Analysis [J]. Scientometrics, 2008, 77(3): 485-504.
[3] Zhao D. Going Beyond Counting First Authors in Author Co-citation Analysis [J]. Proceedings of the American Society for Information Science and Technology, 2005, 42(1). DOI: 10.1002/meet.14504201210.
[4] Zhao D, Strotman A. Evolution of Research Activities and Intellectual Influences in Information Science 1996-2005: Introducing Author Bibliographic-coupling Analysis [J]. Journal of the American Society for Information Science and Technology, 2008, 59(13): 2070-2086.
[5] 刘志辉, 张志强. 作者关键词耦合分析方法及实证研究[J].情报学报, 2010, 29(2): 268-275. (Liu Zhihui, Zhang Zhiqiang. Author Keyword Coupling Analysis: An Empirical Research [J]. Journal of the China Society for Scientific and Technical Information, 2010, 29(2): 268-275.)
[6] 邱均平, 陈木佩. 我国计量学领域作者合作关系研究[J].
情报理论与实践, 2012, 35(11): 56-60. (Qiu Junping, Chen Mupei. Study on the Relationship between the Cooperation in the Science of National Metrology [J]. Information Studies: Theory & Application, 2012, 35(11): 56-60.)
[7] 陈远, 王菲菲. 基于CSSCI的国内情报学领域作者文献耦合分析[J]. 情报资料工作, 2011(5): 6-12. (Chen Yuan, Wang Feifei. An Analysis on the Bibliographic Coupling in the Field of Information Studies in China: Based on CSSCI [J]. Information and Documentation Services, 2011(5): 6-12.)
[8] 陈卫静, 郑颖. 基于作者关键词耦合的潜在合作关系挖掘[J]. 情报杂志, 2013, 21(5): 127-131. (Chen Weijing, Zheng Ying. Mining Potential Cooperative Relationships Based on the Author Keyword Coupling Analysis [J]. Journal of Intelligence, 2013, 21(5): 127-131.)
[9] 沈耕宇, 黄水清, 王东波. 以作者合作共现为源数据的科研团队发掘方法研究[J]. 现代图书情报技术, 2013(1): 57-62. (Shen Gengyu, Huang Shuiqing, Wang Dongbo. On the Scientific Research Teams Identification Method Taking Co- authorship of Collaboration as the Source Data [J]. New Technology of Library and Information Service, 2013(1): 57-62.)
[10] 孙鸿飞, 侯伟, 周兰萍, 等. 近五年我国情报学研究方法应用的统计分析[J]. 情报科学, 2014, 32(4): 77-84. (Sun Hongfei, Hou Wei, Zhou Lanping, et al. Statistical Analysis of Application of Research Methods of Information Science in Our Country in Recent Five Years [J]. Information Science, 2014, 32(04): 77-84.)
[11] 邱均平. 信息计量学[M]. 武汉: 武汉大学出版社, 2007: 916-937. (Qiu Junping. Information Metrology [M]. Wuhan: Wuhan University Press, 2007: 916-937.)

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