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New Technology of Library and Information Service  2011, Vol. 27 Issue (1): 83-87    DOI: 10.11925/infotech.1003-3513.2011.01.13
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Evolution of Topics About Medical Informatics by Improved Co-word Cluster Analysis
Yang Ying1, Cui Lei2
1. Library of China Medical University, Shenyang 110001,China;
2. Department of Information Management and Information System (Medicine), China Medical University, Shenyang 110001,China
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Co-word cluster method is improved by following ways: high-frequency words are selected according to the formula derived from Zipf’s law; adhesive force is used to identify the core major MeSH words for tagging the content of each cluster; contrastive analysis of two periods helps to find the topics change. The bibliographic data of medical informatics are collected from PubMed in two periods (1999-2003 and 2004-2008). Major MeSH words from the articles are extracted separately to make co-word clusters as to explore the evolution of this subject structure based on comparison of two periods.

Key wordsCo-word analysis      Visualization      Cluster      Adhesive force      Zipf’s law     
Received: 26 September 2010      Published: 12 February 2011



Cite this article:

Yang Ying, Cui Lei. Evolution of Topics About Medical Informatics by Improved Co-word Cluster Analysis. New Technology of Library and Information Service, 2011, 27(1): 83-87.

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[1] 朱安青, 周金元. 我国科技查新研究热点及趋势分析—共词分析视角
[J]. 图书情报研究 , 2009, 2(4): 45-49.

[2] 张勤, 马费成. 国内知识管理结构研究探讨—以共词分析为方法
[J]. 情报科学 , 2008(1): 93-101.

[3] 楼雯, 屈卫群, 李立从. 基于共词分析法的我国竞争情报研究主题分析
[J]. 情报杂志 , 2009, 28(Z2):88-90.

[4] Ohniwa R L, Hibino A, Takeyasu K. Trends in Research Foci in Life Science Fields over the Last 30 Years Monitored by Emerging Topics
[J]. Scientometrics, 2010, 85(1): 111-127.

[5] Janssens F, Leta J, Glanzel W, et al.Towards Mapping Library and Information Science
[J]. Information Processing & Management,2006, 42(6):1614-1642.

[6] 李长玲,翟雪梅.基于硕士学位论文的我国图书馆学与情报学研究热点分析
[J]. 情报科学 , 2008(7):1056-1060.

[7] Sardinha, Andrew P B. Applications of WordSmith Keywords
[J]. Liverpool Working Papers in Applied Linguistics, 1996, 2(1): 81-90.

[8] 马费成,望俊成,陈金霞,等.我国数字信息资源研究的热点领域:共词分析透视
[J]. 理论与探索 , 2007,30(4):438-443.

[9] Donohue J C. Understanding Scientific Literatures—A Bibliometric Approach
[M]. Cambridge: The MIT Press,1973: 49–50.

[10] 钟伟金. 共词聚类分析法的类团实例研究——对肿瘤治疗热点主题的分析
[J]. 中华医学图书情报杂志 , 2009, 18(2): 48-53.

[11] 崔雷, 刘伟, 闫雷, 等. 文献数据库中书目信息共现挖掘系统的开发
[J]. 现代图书情报技术 , 2008(8):70-75.

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