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Table 3.

Hyperparameters we explored for each algorithm.

Hyperparameters Tested values Best value
Affinity propagation
affinity euclidean euclidean
convergence_iter 20 20
damping [0.5, 0.51,..., 0.99] 0.5
max_iter 200 200

Agglomerative clustering
affinity [manhattan, cosine,
euclidean] manhattan
compute_full_tree [False, True] False
linkage [complete, average] complete
n_clusters [9, 10,..., 26] 26

Bayesian Gaussian mixture
covariance_type [tied, diag] diag
init_params [kmeans, random] kmeans
n_components [9, 10,..., 26] 20
n_init 5 5
random_state ( † ) [31, 43,..., 473] 283
warm_start [False, True] False

DBSCAN
algorithm [ball_tree, kd_tree,
brute] ball_tree
eps [0.3, 0.31,..., 1.49] 0.58
leaf_size 10 10
metric euclidean euclidean
min_samples [2, 3] 2

Gaussian mixture
covariance_type [spherical, diag] spherical
init_params [k-means, random] k-means
max_iter 1000 1000
n_components [2, 3,..., 26] 20
random_state [31, 43,..., 473] 283

K-means
init [k-means++, random] random
n_clusters [9, 10,..., 26] 26
n_init 5 5
random_state ( † ) [31, 43,..., 473] 43

Mini-batch K-means
batch_size [10, 20,..., 100] 90
init [k-means++, random] random
n_clusters [9, 10,..., 26] 25
random_state ( † ) [31, 43,..., 473] 31
reassignment_ratio [0.1, 0.01, 0.001] 0.1

Spectral clustering
affinity [rbf, sigmoid,
polynomial, poly] rbf
assign_labels [kmeans, discretize] discretize
degree [3, 4, 5] 3
n_clusters [9, 10,..., 26] 17
n_neighbors [2, 5, 10] 2
random_state ( † ) [31, 43,..., 473] 383

Notes. The last column shows the value of the parameter in the run with the highest homogeneity score.

( † )We have tried ten different random seeds, 31, 43, 98, 196, 283, 294, 374, 383, 433 and 473.

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