基于追随者-领导者聚类算法（FLCA）的聚类分析综合方法：文献计量分析。

BACKGROUND: There are 3 issues in bibliometrics that need to be addressed: The lack of a clear definition for author collaborations in cluster analysis that takes into account collaborations with and without self-connections; The need to develop a simple yet effective clustering algorithm for use in coword analysis, and; The inadequacy of general bibliometrics in regard to comparing research achievements and identifying articles that are worth reading and recommended for readers. The study aimed to put forth a clustering algorithm for cluster analysis (called following leader clustering [FLCA], a follower-leading clustering algorithm), examine the dissimilarities in cluster outcomes when considering collaborations with and without self-connections in cluster analysis, and demonstrate the application of the clustering algorithm in bibliometrics. METHODS: The study involved a search for articles and review articles published in JMIR Medical Informatics between 2016 and 2022, conducted using the Web of Science core collections. To identify author collaborations (ACs) and themes over the past 7 years, the study utilized the FLCA algorithm. With the 3 objectives of; Comparing the results obtained from scenarios with and without self-connections; Applying the FLCA algorithm in ACs and themes, and; Reporting the findings using traditional bibliometric approaches based on counts and citations, and all plots were created using R. RESULTS: The study found a significant difference in cluster outcomes between the 2 scenarios with and without self-connections, with a 53.8% overlap (14 out of the top 20 countries in ACs). The top clusters were led by Yonsei University in South Korea, Grang Luo from the US, and model in institutes, authors, and themes over the past 7 years. The top entities with the most publications in JMIR Medical Informatics were the United States, Yonsei University in South Korea, Medical School, and Grang Luo from the US. CONCLUSION: The FLCA algorithm proposed in this study offers researchers a comprehensive approach to exploring and comprehending the complex connections among authors or keywords. The study suggests that future research on ACs with cluster analysis should employ FLCA and R visualizations.

背景：文献计量学存在 3 个问题需要解决：在聚类分析中，缺乏考虑有自我连接和无自我连接的合著者合作的明确定义；需要开发一种简单而有效的聚类算法，用于共词分析；以及一般文献计量学在比较研究成果和确定值得读者阅读和推荐的文章方面的不足。本研究旨在提出一种聚类算法（称为跟随领导者聚类[FLCA]，一种追随者-领导者聚类算法）用于聚类分析，检查在聚类分析中考虑有自我连接和无自我连接的合著者合作时聚类结果的差异，并展示聚类算法在文献计量学中的应用。

方法：本研究使用 Web of Science 核心集搜索了 2016 年至 2022 年期间在 JMIR 医学信息学上发表的文章和综述文章，以识别过去 7 年的作者合作（AC）和主题，使用 FLCA 算法。研究的 3 个目标是：比较有自我连接和无自我连接的情景的结果；在 AC 和主题中应用 FLCA 算法；使用基于计数和引文的传统文献计量方法报告发现，所有图表均使用 R 创建。

结果：研究发现，在有自我连接和无自我连接的 2 种情景之间，聚类结果存在显著差异，重叠率为 53.8%（AC 排名前 20 位的国家中有 14 个）。顶级聚类由韩国延世大学、美国的 Grang Luo 和过去 7 年的模型研究所、作者和主题引领。在 JMIR 医学信息学上发表文章最多的顶级实体是美国、韩国延世大学、医学院和美国的 Grang Luo。

结论：本研究提出的 FLCA 算法为研究人员提供了一种全面的方法来探索和理解作者或关键字之间的复杂联系。研究建议，未来的 AC 聚类分析研究应采用 FLCA 和 R 可视化。