from sklearn.decomposition import 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[:, ...
# 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) #...
# 应用KMeans进行聚类分析 kmeans=KMeans(n_clusters=3, random_state=42) kmeans_labels=kmeans.fit_predict(principal_components) pca_df['Cluster'] =kmeans_labels # 计算解释方差得分 variance_score=explained_variance_score(features_scaled, pca.inverse_transform(principal_components)) print("Explained ...
# 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....
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) ...
random_state=42) y_pred = KMeans(n_clusters=3,random_state=42).fit_predict(X_varied) mglearn.discrete_scatter(X_varied[:,0],X_varied[:,1],y_pred) plt.legend(["cluster0","cluster1","cluster2"],loc='best') plt.xlabel("Feature0") ...
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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...
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 ...
# 根据不同的n_centers进行聚类 n_clusters=[xforxinrange(3,6)]foriinrange(len(n_clusters)):# 实例化k-means分类器 clf=KMeans(n_clusters=n_clusters[i])y_predict=clf.fit_predict(x_true)# 绘制分类结果 plt.figure(figsize=(6,6))plt.scatter(x_true[:,0],x_true[:,1],c=y_predict,...