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.
Objective This paper aimed to examine the reliability of co-citation clustering analysis in representing the research history of subject
by comparing the results from co-citation clustering analysis with a review written by authorities.
Methods Firstly, the treatment of traumatic spinal cord injury was chosen as an investigated subject to be retrieved the resource
articles and their references were downloaded from Science Citation Index CD-ROM between 1992 and 2002. Then, the highly cited
papers were arranged chronologically and clustered with the method of co-citation clustering. After mapping the time line
visualization, the history and structure of treatment of spinal cord injury were presented clearly. At last, the results and
the review were compared according the time period, and then the recall and the precision were calculated.
Results The recall was 37.5%, and the precision was 54.5%. The research history of traumatic spinal cord injury treatment analyzed
by co-citation clustering was nearly consistent with authoritative review, although some clusters had shorter period than
which was summarized by professionals.
Conclusion This paper concluded that co-citation clustering analysis was a useful method in representing the research history of subject,
especially for the information researchers, who do not have enough professional knowledge. Its demerit of low recall could
be offset by combination this method with other analytic techniques.