cluster=KMeans(n_clusters=3,random_state=123).fit(x_) # 3.1 获取聚类质心 center=cluster.cluster_centers_ # [[0.75733298 0.79374354 0.69419238 0.73003765 0.76950062 0.36757645, 0.75709318], [0.1233337 0.17513685 0.37817899 0.18671025 0.16252742 0.49856915, 0.27928792], [0.38349003 0.4198407 0.67120387 0.3646...
sklearn.metrics.silhouette_score(X, labels, *, metric='euclidean', sample_size=None, random_state=None, **kwds) 1. 2. 示例: # Initialize the clusterer with n_clusters value and a random generator # seed of 10 for reproducibility. clusterer = KMeans(n_clusters=n_clusters, random_state=...
scikit-learn中用于进行k-means机器学习的类是sklearn.cluster.KMeans,它所涉及的参数有超过10个之多,但是最常用的其实就是n_clusters 和random_state; n_clusters表示打算聚类的数目,默认情况下是8; random_state表示产生随机数的方法。默认缺省值为None,此时随机数产生器是np.random所使用的RandomState实例。可以自...
model_kmeans = KMeans(n_clusters=n_clusters, random_state=0) # 建立聚类模型对象 model_kmeans.fit(scaled_numeric_features) # 训练聚类模型 # 4.模型评估 n_samples, n_features = raw_data.iloc[:, 1:].shape print('样本数:%d , 特征数:%d' % (n_samples, n_features)) score = silhouett...
y_pred = KMeans(n_clusters=2, random_state=9).fit_predict(X) # 分别尝试n_cluses=2\3\4,然后查看聚类效果 plt.scatter(X[:, 0], X[:, 1], c=y_pred) plt.show() # ⽤Calinski-Harabasz Index评估的聚类分数 print(calinski_harabaz_score(X, y_pred)) ...
y_pred=KMeans(n_clusters=2,random_state=9).fit_predict(X)plt.scatter(X[:,0],X[:,1],c=y_pred)plt.show() k=2聚类的效果图输出如下: 现在我们来看看我们用Calinski-Harabasz Index评估的聚类分数: 代码语言:javascript 复制 from sklearnimportmetrics ...
一个random_state对应一个质心随机初始化的随机数种子。如果不指定随机数种子,则sklearn中的K-Means并不会只选择一个随机模式扔出结果,而会在每个随机数种子下运行多次,并使用结果最好的一个随机数种子来作为初始质心。 在sklearn中也可以使用参数n_init来选择(每个随机数种子下运行的次数),可以增加这个参数n_init...
KMeans(n_clusters=8,init='k-means++',n_init=10,max_iter=300,tol=0.0001,precompute_distances='auto',verbose=0,random_state=None,copy_x=True,n_jobs=1,algorithm='auto') 总结 如何区分k-means与knn: k-means是聚类算法,knn是有监督的分类算法;聚类没有标签,分类有标签 ...
KMeans(n_clusters=8, init='k-means++', n_init=10, max_iter=300, tol=0.0001, precompute_distances='auto', verbose=0, random_state=None, copy_x=True, n_jobs=1, algorithm='auto') 总结: 如何区分k-means与knn: k-means是聚类算法,knn是有监督的分类算法;聚类没有标签,分类有标签 ...
The first initial mean is selected at random from all data items. The initial means are existing data items and they are sometimes called medoids.Next, a for loop is constructed to select the remaining k-1 means:C# Copy for (int k = 1; k < numClusters; ++k) { double[] dSquared ...