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通过开发追随者-领导者集群算法和确定顶级共同作者国家来分析作者合作:集群分析.

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科学领域:

  • 圣经计量和科学计量.
  • 在科学研究中的网络分析.
  • 数据挖掘和可视化在医学中的数据.

背景情况:

  • 了解作者协作 (ACs) 和共同词分析在文献计量学中至关重要.
  • 现有的集群算法可能无法完全捕捉到ACs的动态.
  • 识别经常共同撰写的国家为全球研究趋势提供了洞察力.

研究的目的:

  • 开发和评估一种新的集群算法,即追随者领先的集群算法 (FLCA),用于分析作者协作 (AC) 和共同词.
  • 调查医学领域各国之间的国际共同作者模式.
  • 为了展示FLCA和可视化技术的应用,用于参考资料分析.

主要方法:

  • 从Web of Science提取的文章元数据用于医学杂志 (巴尔的摩) 从2020-2022.
  • 使用R统计软件实现了追随者领先的集群算法 (FLCA).
  • 利用网络图,热图与树状图,Venn图和和弦图来可视化和分析AC和共同词.

主要成果:

  • 该FLCA算法有效地识别了库存数据中的集群.
  • 美国,中国,韩国,日本和西班牙是2020-2022年国际共同作者排名前五的国家.
  • 中国,四川大学和"诊断"分别是国家,机构和关键词中的领先实体,基于AC和共同词.

结论:

  • 拟议的FLCA算法提供了一个强大的方法来探索复杂的作者和关键字关系.
  • 使用R生成的可视化增强了对AC和同词网络的理解.
  • 建议在未来对作者合作和共同词的文献计量研究中使用FLCA和集群分析.