%0 Journal Article
%T Evolution of Topics About Medical Informatics by Improved Co-word Cluster Analysis<br>应用改进的共词聚类法探索医学信息学热点主题演变
%A Yang Ying Cui Lei
%A China Medical University
%A China
%A <br>杨颖
%A 崔雷
%J 现代图书情报技术
%D 2011
%I 
%X 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.
%K Co-word analysis
%K Visualization
%K Cluster
%K Adhesive force
%K Zipf&rsquo
%K s law<br>共词分析
%K 可视化
%K 聚类
%K 粘合力
%K 齐普夫定律
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=B5EDD921F3D863E289B22F36E70174A7007B5F5E43D63598017D41BB67247657&cid=E46382710BF131B2&jid=24AADBCD0D5373C73F37F78D10E2F717&aid=5704D2E38E2B920F203459B45E9394A8&yid=9377ED8094509821&vid=DB817633AA4F79B9&iid=CA4FD0336C81A37A&sid=06EA2770E96C5402&eid=117F81797AB182FC&journal_id=1003-3513&journal_name=现代图书情报技术&referenced_num=0&reference_num=0