Table 1 Clustering methods tested for robustness
From: A robustness metric for biological data clustering algorithms
Algorithm | Type | Setting | Implementation |
|---|---|---|---|
Average | Hierarchical | Number of clusters | R 3.2.3 |
Complete | Hierarchical | Number of clusters | R 3.2.3 |
Mcquitty | Hierarchical | Number of clusters | R 3.2.3 |
Ward | Hierarchical | Number of clusters | R 3.2.3 |
CLICK | Graph-based | Cluster homogeneity | Expander4 |
NNN | Graph-based | Min neighborhood size | Java |
Paraclique | Graph-based | Starting clique | C++ |
WGCNA | Graph-based | Power | R 3.2.3 |
K-means | Partitioning | Number of clusters | R 3.2.3 |
QT Clustering | Partitioning | Max cluster diameter | R 3.2.3 |
SOM | Neural network | Grid type/size | R 3.2.3 |