视觉冲击束图:使用以下领先的集群算法 (FLCA) 分析研究概况和文献计量指标
Yung-Ze Cheng1, Tsair-Wei Chien2, Sam Yu-Chieh Ho1,3
1Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan.
Medicine
|July 14, 2023
概括
本研究介绍了冲击束图 (IBP) 用于使用以下领先的集群算法 (FLCA) 可视化作者出版物. 实际上,FLCA生成IBP,提供了一种代表研究影响和引用模式的新方法.
科学领域:
- 图书统计学 图书统计学
- 科学测量公司的科学测量.
- 信息科学 信息科学 信息科学
背景情况:
- 传统的出版物列表正在被视觉表示所取代.
- 冲击束图 (IBPs) 提供了一个新的研究概况可视化.
- 自引用的文章使用罕见的集群分析进行了整合.
研究的目的:
- 应用以下领先的集群算法 (FLCA) 来生成IBP.
- 为3位高效的作者创建视觉研究资料.
- 通过参考文献计量指标分析出版物影响.
主要方法:
- 从科学网络核心集合下载的出版物为3位作者.
- 利用FLCA将文章聚集在一起,并确定代表性出版物.
- 使用网络图,热图和树状图来验证FLCA.
- 根据h指数,x指数和自我引用率生成并比较IBP.
主要成果:
- 使用FLCA成功为3位作者生成IBP.
- 作者的指标包括h指数,x指数和自我引用率.
- 较高的自我引用率与较低的集群数量表明自己起草的手稿.
结论:
- 在FLCA促进容易生成的视觉IBPs.
- IBP集成h-index,x-index和自我引用,以提供全面的作者资料.
- 这些图书统计指标为学术影响提供了有价值的见解.
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