# 使用PCA进行降维,以便更好地进行聚类分析pca = PCA(n_components=2) # 降至2维以便可视化 X_pca = pca.fit_transform(X_std) # 使用K-means进行聚类 k = 3 # 基于先前的分析决定将用户分为3个群体 kmeans = KMeans(n_clusters=k, random_state=42) y_kmeans = kmeans.fit_predict(X_pca) #...
kmeans = KMeans(n_clusters=3, random_state=42) kmeans_clusters = kmeans.fit_predict(data) pca = PCA(n_components=2) reduced_data = pca.fit_transform(data) plt.figure(figsize=(8, 6)) scatter = plt.scatter(reduced_data[:, 0], reduced_data[:, 1], c=kmeans_clusters, cmap='viri...
X, y = make_blobs(n_samples=500,n_features=2,centers=4,random_state=0) clu=KMeans(n_clusters=4,random_state=10).fit(X) # 聚类结果的每个样本的标签 clu_label=clu.labels_ fig=plt.figure(figsize=(8,5)) ax=fig.add_subplot(111) ax.scatter(X[:, 0] # 所有点的横坐标 , X[:, 1...
def make_Silhouette_plot(X, n_clusters): plt.xlim([-0.1, 1]) plt.ylim([0, len(X) + (n_clusters + 1) * 10]) # 建立聚类模型 clusterer = KMeans(n_clusters=n_clusters, max_iter=1000, n_init=10, init="k-means++", random_state=10) # 聚类预测生成标签label cluster_label = cl...
kmeans=KMeans(n_clusters=k,random_state=42) y_pred=kmeans.fit_predict(X) plt.plot(X[y_pred==1,0],X[y_pred==1,1],"ro",label="group 1") plt.plot(X[y_pred==0,0],X[y_pred==0,1],"bo",label="group 0") #plt.legend(loc=2) ...
# 使用 k-means++ 初始化进行聚类kmeans_pp = KMeans(n_clusters=3, init='k-means++', random_state=42)labels_pp = kmeans_pp.fit_predict(data)centroids_pp = kmeans_pp.cluster_centers_# 数据可视化plt.scatter(data[:, 0], data[:, 1], c=labels_pp, cmap='viridis', marker='o')plt....
我试图在我的数据集中进行聚类,其中有4个数值字段。请查找所附文件:http://www.filedropper.com/example_3.我试过用这个代码:kmeans = KMeans(n_clusters=2, random_state=0, max_iter =300).fit(dffinal) 我知道在这个例子中有两个类,这就是我尝试使用两个集群的原因。在42 浏览0提问于2016-12-23得...
kmeans = KMeans(n_clusters=n_clusters, init='k-means++', random_state=42, n_jobs=-1) ...
fromsklearn.datasetsimportmake_blobsX,y_true=make_blobs(n_samples=300,centers=4,cluster_std=0.6,random_state=0)plt.scatter(X[:,0],X[:,1],s=50);fromsklearn.clusterimportKMeans kmeans=KMeans(n_clusters=4)kmeans.fit(X)y_kmeans=kmeans.predict(X)plt.figure()plt.scatter(X[:,0],X...
Discussed in #27182 Originally posted by Somesh140 August 27, 2023 `# elbow method clustering_score = [] for i in range(1,11): kmeans = KMeans(n_clusters=i,init = 'random',n_init='auto',random_state = 42) kmeans.fit(X) clustering_score.a...