种类	模型	参数	fit	字段	n_jobs	官网
cluster	Kmeans	n_clusters init : {‘k-means++’, ‘random’ or an ndarray}选取初始点的方法 n_init : int, default: 10重新运行的次数 verbose : int, default 0打印日志	fit(X[, y]) Compute k-means clustering. fit_predict(X[, y]) Compute cluster centers and predict cluster index for each sample. fit_transform(X[, y]) Compute clustering and transform X to cluster-distance space. predict(X) Predict the closest cluster each sample in X belongs to. score(X[, y]) Opposite of the value of X on the K-means objective. transform(X) Transform X to a cluster-distance space.		有	http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html
	MiniBatchKMeans					http://scikit-learn.org/stable/modules/generated/sklearn.cluster.MiniBatchKMeans.html
	AgglomerativeClustering	n_clusters : int, default=2 affinity : string or callable, :  “euclidean”(default), “l1”, “l2”, “manhattan”, “cosine”, or ‘precomputed’.     If linkage is “ward”, only “euclidean” is accepted. linkage : {“ward”, “complete”, “average”}	fit(X[, y]) Fit the hierarchical clustering on the data fit_predict(X[, y]) Performs clustering on X and returns cluster labels.
	DBSCAN	eps : float, optional min_samples : int, optional metric : string, or callable metric_params : dict, optional algorithm : {‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, optional p : float, optional：The power of the Minkowski	fit(X[, y, sample_weight]) Perform DBSCAN clustering from features or distance matrix. fit_predict(X[, y, sample_weight]) Performs clustering on X and returns cluster labels.		有	http://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html
mixture	GaussianMixture	n_components : int, defaults to 1. covariance_type : {‘full’, ‘tied’, ‘diag’, ‘spherical’},指定协方差类型     'full' (each component has its own general covariance matrix),     'tied' (all components share the same general covariance matrix),     'diag' (each component has its own diagonal covariance matrix),     'spherical' (each component has its own single variance). n_init : int, defaults to 1.模型重新运行的次数 init_params : {‘kmeans’, ‘random’}, defaults to ‘kmeans’. warmstart	aic(X) Akaike information criterion for the current model on the input X. bic(X) Bayesian information criterion for the current model on the input X. fit(X[, y]) Estimate model parameters with the EM algorithm. predict(X) Predict the labels for the data samples in X using trained model. predict_proba(X) Predict posterior probability of each component given the data. sample([n_samples]) Generate random samples from the fitted Gaussian distribution. score(X[, y]) Compute the per-sample average log-likelihood of the given data X. score_samples(X) Compute the weighted log probabilities for each sample.			http://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html