双聚类方法在同被引分析中应用的研究  

Biclustering in Co-citation Analysis

作　　者：杨颖[1] 崔雷 Yang Ying;Cui Lei(Library of China Medical Universit;College of Medical Information, China Medical Universit)

机构地区：[1]中国医科大学图书馆 [2]中国医科大学医学信息学院

出　　处：《图书馆杂志》2018年第5期67-73,共7页Library Journal

基　　金：辽宁省教育厅科学研究一般项目"大数据时代高校图书馆面向重点学科的综合情报服务研究"(项目编号:W2014106)的研究成果之一

摘　　要：目的:本文将双聚类的方法用于同被引分析,即在同被引分析中充分考虑列属性的特点,在行和列两个方向,对高频同被引文献及其引用文献进行聚类分析,同时实现同被引聚类分析和基于高被引论文的引文耦合分析,可探索某领域的基础结构与研究热点。方法:本文以WebofScience网络数据库中的护理研究文献为例,对高频被引论文进行同被引双向聚类分析。结果与结论:聚类结果通过g Cluto软件进行可视化显示,并结合护理研究知识对聚类结果进行分析解释,总结该领域的学科基础结构和研究热点。Purpose： In this paper, biclustering was used in co-citation analysis. Co-citations with high frequency and citations were clustered at the same time, so that co-citation analysis and citation coupling analysis were performed simultaneously to explore the intellectual base and research front of certain field. Method： Co-citation biclustering analysis of the highly cited papers was performed using the papers on nursing in Web of Science as data source. Result and conclusion： The visualization results were visualized by g Cluto. The intellectual base of nursing research and its hot research spots were summarized.

关 键 词：同被引分析 双聚类 gCluto 

分 类 号：G353.1[文化科学—情报学]