英文摘要 |
Many studies have used social networks to explore the classification of author collaboration teams or keywords, but appropriate cluster analysis methods have not been applied. This study uses a cluster algorithm developed by the authors (referred to as the follower-leading clustering algorithm, FLCA) and chord diagram drawing to classify research topics based on journal author keywords. From the ''Taiwan Medical Information Journal'' database of the Airiti Library, 605 author names and their paper keywords were downloaded and analyzed using the FLCA algorithm. The team (or theme) with the most papers was selected, and the highest degree of centrality within the cluster was used as the representative name. Based on the keywords of the team's papers, their topic classification was compared, and the research results were presented using chord diagrams. The results showed that the cluster algorithm developed by the authors only uses the maximum unique link between followers and leaders, so the links between keyword clusters cannot be seen. The chord diagram displays the clustering phenomenon of the top 20 keywords, with electronic medical records, information modules, diabetes, data warehousing, speech recognition, and decision support systems as the leading topics. Based on the classification of paper keywords, this study demonstrates how chord diagrams can display the relationships between clusters, providing a reference for visualizing team collaboration and topic classification within organizations in the future. |