sklearn.cluster.k_means(X, n_clusters, init='k-means++', precompute_distances='auto', n_init=10, max_iter=300, verbose=False, tol=0.0001

sklearn.cluster.ward_tree(X, connectivity=None, n_clusters=None, return_distance=False)

sklearn.cluster.affinity_propagation(S, preference=None, convergence_iter=15, max_iter=200, damping=0.5, copy=True, verbose=False

class sklearn.cluster.MeanShift(bandwidth=None, seeds=None, bin_seeding=False, min_bin_freq=1, cluster_all=True, n_jobs=1)

class sklearn.cluster.AgglomerativeClustering(n_clusters=2, affinity='euclidean', memory=Memory(cachedir=None), connectivity=None

class sklearn.cluster.MiniBatchKMeans(n_clusters=8, init='k-means++', max_iter=100, batch_size=100, verbose=0, compute_labels=True

sklearn.cluster.mean_shift(X, bandwidth=None, seeds=None, bin_seeding=False, min_bin_freq=1, cluster_all=True, max_iter=300, n_jobs=1)

class sklearn.cluster.AffinityPropagation(damping=0.5, max_iter=200, convergence_iter=15, copy=True, preference=None, affinity='euclidean'

class sklearn.cluster.Birch(threshold=0.5, branching_factor=50, n_clusters=3, compute_labels=True, copy=True)

class sklearn.cluster.bicluster.SpectralCoclustering(n_clusters=3, svd_method='randomized', n_svd_vecs=None, mini_batch=False