4 用scikit-learn学习DBSCAN聚类 （基于密度的聚类）
6 混合高斯模型Gaussian Mixture Model(GMM)
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从左到右依次为： k-means聚类， DBSCAN聚类 ， GMM聚类
对应代码：
# kmeans聚类 from sklearn.cluster import KMeans estimator = KMeans(n_clusters=2)#构造聚类器 y_pred =estimator.fit_predict(X_train_2)#聚类 clr = ['b' if i==0 else 'y' if i==1 else 'r' for i in y_pred] plt.scatter(X_train[:,0],X_train[:,1],c=clr) # DBSCAN（Density-Based Spatial Clustering of Application with Noise）基于密度的空间聚类算法 from sklearn.cluster import DBSCAN dbs1 = DBSCAN(eps=0.5, # 邻域半径 min_samples=5 ) # 最小样本点数，MinPts y_pred = dbs1.fit_predict(X_train_2) #训练集的标签 clr = ['b' if i==0 else 'y' if i==1 else 'r' for i in y_pred] plt.scatter(X_train_2[:,0],X_train_2[:,1],c=clr) #混合高斯模型Gaussian Mixture Model(GMM)聚类 from sklearn import mixture clf = mixture.GaussianMixture(n_components=2,covariance_type='full') clf.fit(X_train_2) #.fit_predict y_pred = clf.predict(X_train_2) #预测 clr = ['b' if i==0 else 'y' if i==1 else 'r' for i in y_pred] plt.scatter(X_train_2[:,0],X_train_2[:,1],c=clr)