Table 6.
Top ranking 67 covariate clustering methods from clusterSim
| Index metric | Index value | Distance measure | Clustering method | No. of clusters |
|---|---|---|---|---|
| Silhouette |
0.6692 |
Generalized Distance Measure |
Partitioning Around Medoids |
2 |
| Baker & Hubert |
0.9122 |
Chebyschev |
Hierarchical - Single linkage |
2 |
| Hubert & Levine |
0.0279 |
Generalized Distance Measure |
Partitioning Around Medoids |
24 |
| Generalized Distance Measure |
Hierarchical - Average linkage |
8 |
||
| Generalized Distance Measure |
Hierarchical - Average linkage |
14 |
||
| Generalized Distance Measure | Hierarchical - Average linkage | 13 | ||
The optimal distance measure and clustering method using three separate indices are shown along with the associated index value in each case. Where no index metric or value is given, an attempt was made to create more informative clusters rather than optimize a clustering index.