Integration of three visualization methods based on co-word analysis

Authors:
Ying Yang;Mingzhi Wu;Lei Cui
Affiliations:
Library of China Medical University, Shenyang, China 110001;Library of Shenyang Pharmaceutical University, Shenyang, People's Republic of China;Department of Medical Informatics and Information System, China Medical University, Shenyang, People's Republic of China
Venue:
Scientometrics
Year:
2012

Citing 4
Cited 2

Bibliometric cartography of information retrieval research by using co-word analysis

Information Processing and Management: an International Journal
MedlineR: an open source library in R for Medline literature data mining

Bioinformatics
CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature

Journal of the American Society for Information Science and Technology
A co-classification approach to learning from multilingual corpora

Machine Learning

Doctoral dissertations of Library and Information Science in China: A co-word analysis

Scientometrics
Global remote sensing research trends during 1991---2010: a bibliometric analysis

Scientometrics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Visualization of subject structure based on co-word analysis is used to explore the concept network and developmental tendency in certain field. There are many visualization methods for co-word analysis. However, integration of results by different methods is rarely reported. This article addresses the knowledge gap in this field of study. We compare three visualization methods: Cluster tree, strategy diagram and social network maps, and integrate different results together to one result through co-word analysis of medical informatics. The three visualization methods have their own character: cluster trees show the subject structure, strategic diagrams reveal the importance of topic themes in the structure, and social network maps interpret the internal relationship among themes. Integration of different visualization results to one more readable map complements each other. And it is helpful for researchers to get the concept network and developmental tendency in a certain field.