sklearn.cluster聚类模块结构（classes||Functions）及用法

• sklearn.cluster官方文档 各种聚类算法详解

sklearn.cluster模块提供了常用的非监督聚类算法。

该模块中每一个聚类算法都有两个变体: 一个是类(class)另一个是函数(function)。 类实现了 fit 方法来从训练数据中学习聚类；函数接受训练数据返回对应于不同聚类的一个整数标签数组。 对类来说，训练过程得到的标签数据可以在属性 labels_ 中找到。

• 模块内的各种类class

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.

• 模块的函数functions

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.

• Reference

1. 2.3. 聚类(Clustering) 中文文档