sklearn.cluster模块提供了常用的非监督聚类算法。
class |
describe |
cluster.AffinityPropagation ([damping, …]) |
Perform Affinity Propagation Clustering of data. |
cluster.AgglomerativeClustering ([…]) |
Agglomerative Clustering |
cluster.Birch ([threshold, branching_factor, …]) |
Implements the Birch clustering algorithm. |
cluster.DBSCAN ([eps, min_samples, metric, …]) |
Perform DBSCAN clustering from vector array or distance matrix. |
cluster.FeatureAgglomeration ([n_clusters, …]) |
Agglomerate features. |
cluster.KMeans ([n_clusters, init, n_init, …]) |
K-Means clustering |
cluster.MiniBatchKMeans ([n_clusters, init, …]) |
Mini-Batch K-Means clustering |
cluster.MeanShift ([bandwidth, seeds, …]) |
Mean shift clustering using a flat kernel. |
cluster.SpectralClustering ([n_clusters, …]) |
Apply clustering to a projection to the normalized laplacian. |
functions |
describe |
cluster.affinity_propagation (S[, …]) |
Perform Affinity Propagation Clustering of data |
cluster.dbscan (X[, eps, min_samples, …]) |
Perform DBSCAN clustering from vector array or distance matrix. |
cluster.estimate_bandwidth (X[, quantile, …]) |
Estimate the bandwidth to use with the mean-shift algorithm. |
cluster.k_means (X, n_clusters[, …]) |
K-means clustering algorithm. |
cluster.mean_shift (X[, bandwidth, seeds, …]) |
Perform mean shift clustering of data using a flat kernel. |
cluster.spectral_clustering (affinity[, …]) |
Apply clustering to a projection to the normalized laplacian. |
cluster.ward_tree (X[, connectivity, …]) |
Ward clustering based on a Feature matrix. |