Table 1.
Data used in the clustering analysis and the values corresponding to the optimal partition for each of the three hierarchical clustering methods used
Optimal_k, the optimal number of clusters as identified based on the silhouette, McClain–Rao and Dunn2 index; diameter, the maximum within-cluster distance; separation, minimum between-clusters distance.
|
Workflow |
Clustering |
Optimal_k |
Diameter |
Separation |
|---|---|---|---|---|
|
SNP1 |
Average |
k14 |
14 |
14 |
|
SNP2 |
Average |
k12 |
16 |
3 |
|
MLSTcg1 |
Average |
k12 |
13 |
10 |
|
MLSTcg2 |
Average |
k13 |
15 |
9 |
|
MLSTcg3 |
Average |
k13 |
13 |
12 |
|
MLSTwg |
Average |
k13 |
13 |
10 |
|
SNP1 |
Complete |
k14 |
14 |
14 |
|
SNP2 |
Complete |
k13 |
13 |
1 |
|
MLSTcg1 |
Complete |
k13 |
9 |
6 |
|
MLSTcg2 |
Complete |
k13 |
11 |
7 |
|
MLSTcg3 |
Complete |
k13 |
13 |
12 |
|
MLSTwg |
Complete |
k13 |
18 |
10 |
|
SNP1 |
Single |
k14 |
14 |
14 |
|
SNP2 |
Single |
k12 |
16 |
3 |
|
MLSTcg1 |
Single |
k11 |
17 |
10 |
|
MLSTcg2 |
Single |
k13 |
15 |
9 |
|
MLSTcg3 |
Single |
k13 |
13 |
12 |
|
MLSTwg |
Single |
k13 |
13 |
10 |