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.
杨颖, 崔雷. 应用改进的共词聚类法探索医学信息学热点主题演变[J]. 现代图书情报技术, 2011, 27(1): 83-87.
Yang Ying, Cui Lei. Evolution of Topics About Medical Informatics by Improved Co-word Cluster Analysis. New Technology of Library and Information Service, DOI：10.11925/infotech.1003-3513.2011.01.13.