Table 3.
Institutions Engaged in Acupuncture for Migraine That Details of Knowledge Clusters
| Cluster ID | Size | Silhouette | Mean (Year) | Label (LLR) | Label (MI) |
|---|---|---|---|---|---|
| 0 | 14 | 0.954 | 2007 | Migraine prophylaxis | Placebo effect |
| 1 | 13 | 0.865 | 2015 | Chronic pain | Placebo effect |
| 2 | 12 | 0.954 | 2012 | Controlled trial | Placebo effect |
| 3 | 11 | 0.848 | 2012 | Pet-ct study | Placebo effect |
| 4 | 10 | 0.936 | 2012 | Altered periaqueductal gray resting state | Clinical trial |
| 7 | 7 | 0.976 | 2012 | Herbal formula granule | Clinical trial |
| 8 | 6 | 0.919 | 2004 | Chronic headache | Clinical trial |
| 9 | 6 | 0.979 | 2005 | Chinese diagnose | Clinical trial |
| 10 | 5 | 0.920 | 2009 | Clinical trial | Placebo effect |
| 11 | 4 | 0.983 | 2018 | Economic analysis | Clinical trial |
Note: 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.