Open Access
Table B.1
Overview of the selected clustering algorithms, their hyperparameters, and their clustering types.
| Algorithm | Hyperparameters | Type |
|---|---|---|
| KMeans | init: [’k-means++’, ’random’], max_iter: [30000], tol: [1e-5] | Hard |
| GMM | covariance_type: [’full’, ’diag’, ’tied’, ’spherical’], tol: [1e-5], max_iter: [30000] | Soft |
| Bayesian GMM | covariance_type: [’full’, ’diag’, ’tied’, ’spherical’], tol: [1e-5], max_iter: [30000] | Soft |
| Agglomerative | linkage: [’ward’, ’complete’, ’average’, ’single’] | Hard |
| BIRCH | threshold: np.linspace(0.1, 0.5, 41) | Hard |
| Spectral Clustering | affinity: [’nearest_neighbors’, ’rbf’] | Hard |
| DBSCAN | eps: np.linspace(0.1, 2, 20), min_samples: [5, 10, 15] | Hard |
| OPTICS | min_samples: [5,10,15], xi: [0.05, 0.1, 0.2], min_cluster_size: [0.05, 0.1, 0.2] | Hard |
Notes. The notation np.linspace(start, stop, num) indicates an array of num evenly spaced numbers starting with start and ending with stop.
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