부산대학교 도서관

This paper proposes a novel algorithm for analyzing author collaborations in scholarly journals using cluster analysis and visual displays. The algorithm called follower-leader clustering (FLC) is implemented using the statistical software R, which allows for efficient and reproducible analysis of large datasets. It utilizes a hierarchical clustering approach to group authors according to (1) their principal connection to the potential leader and (2) their involvement in the leader's activities. Visual displays are then used to represent these groups in a clear and concise manner, allowing readers to easily identify patterns in author collaborations. To evaluate the effectiveness of the proposed algorithm, we applied it to a large dataset of scholarly articles from The Journal of Taiwan Association for Medical Informatics (JTAMI). The results show that the algorithm can effectively identify clusters of authors into six clusters, as well as provide valuable insights into the structure of author networks within a given field. We demonstrate the potential of using cluster analysis and visual displays for analyzing author collaborations in scholarly journals. With the proposed algorithm, researchers are able to explore and understand the complex relationships between authors and cowords or cooccurrences, and can easily apply it to a wide range of datasets and fields of study.