Table 4.

Cited authors engaged in acupuncture for cerebral infarction that details of knowledge clusters.

Cluster ID	Size	Silhouette	Mean (Year)	Label (LLR)	Label (MI)
0	25	0.890	2014	Sirt1-foxo1signaling pathway	Electro-acupuncture treatment
1	20	0.908	2013	Rat model	Electro-acupuncture treatment
2	20	0.892	2020	Visualization analysis	Electro-acupuncture treatment
3	20	0.847	2015	Affecting neurogenesis	Electro-acupuncture treatment
4	18	0.924	2005	Subacute stroke rehabilitation	Subacute stroke rehabilitation
5	18	0.967	2009	Diabetes mellitu	Electro-acupuncture treatment
6	16	0.939	2016	Corticospinal tract remodeling	Electro-acupuncture treatment
7	11	0.966	2019	Neuroinflammatory damage	Electro-acupuncture treatment
8	9	0.926	2018	Cerebral ischemic rat	Electro-acupuncture treatment
9	5	0.980	2003	Hippocampal ca1 region	Ischemic stroke
10	5	0.973	2011	Cerebral blood flow	Ischemic stroke
12	3	0.937	2020	Acupuncture alters brain	Ischemic stroke

The cluster analysis results mainly include cluster ID, mean year, size, silhouette, label (LLR), and label (MI). Cluster ID is the number after clustering, and Size represents the number of members contained in the cluster. The larger the size is, the smaller the number. Mean Year represents the average year of the literature in the cluster, which can be used to judge the distance of the cited literature in the cluster. The larger the log-likelihood ratio (LLR) is, the more representative the cluster category; mutual information (MI) is mainly used to represent the relationship between terms and categories in text mining, and it does not consider the frequency of feature words.