在COVID-19期间和之后开发一种新的算法,用于在期刊文章上比较网络中的集群模式:图书法分析分析
Alice-Like Wu1, Julie Chi Chow2,3
1Department of Medical Statistics and Analytics, Coding Research Center, Toronto, Canada.
Medicine
|March 22, 2024
概括
一个新的算法揭示了COVID-19期间和之后的国家,部门和关键字集群中相同的模式. 然而,研究所和作者集群显示了不相似和不同的模式,为文献计量分析提供了洞察力.
科学领域:
- 图书统计和网络分析.
- 信息科学 信息科学
- 数据挖掘是一种数据挖掘.
背景情况:
- 图书计量依赖于大数据集的集群分析.
- 之前没有研究使用集群模式算法来比较两个集群.
- 这项研究通过开发和应用一种新的算法来弥补这一差距.
研究的目的:
- 创建一个集群模式比较算法 (CPCA) 用于文献识别分析.
- 应用CPCA来分析国家,研究所,部门,作者 (CIDA) 和关键词的集群.
- 为了在COVID-19大流行期间和之后比较这些集群.
主要方法:
- 分析了来自医学杂志 (巴尔的摩) 的9499 (2020-2021) 和5943 (2022-2023) 篇文章.
- 开发了一种使用相似系数,协作地图和主题地图的集群模式比较算法 (CPCA).
- 将追随者领先的集群算法 (FLCA) 与其他八种算法进行验证.
主要成果:
- 对于CIDA实体和关键词的相似系数为0.73,0.35,0.80,0.02和0.83.
- 在国家,部门和关键字集群中发现了相同的模式 (>0.70).
- 在研究所和作者集群中观察到不相似 (<0.5) 和不同的 (<0.3) 模式.
结论:
- 该研究成功开发并应用了CPCA用于文献计量集群模式分析.
- 在国家,部门和关键字集群中存在相同的模式,但在研究所和作者集群中没有.
- CPCA为未来的文献计量研究提供了一个框架,以比较不同的集群模式.
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