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Table 1
Clustering algorithms and basic input parameters.
| Method | Input parameters | Metric |
|---|---|---|
| K-means | Data points, number of clusters | Distance between points |
| Mean-shift | Data points | Distance between points |
| Spectral clustering | Data points, number of clusters, number of neighbours | Graph distance |
| Ward hierarchical clustering | Data points, number of clusters, linkage | Distance between points |
| Agglomerative clustering | Data points, number of clusters, linkage, affinity | Any pairwise distance |
| Gaussian mixtures | Data points, number of mixture components | Mahalanobis distances to centres |
| Birch | Data points, number of clusters, threshold, branching factor | Euclidean distance between points |
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