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) #...
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 Variance Score (Reconstructi...
# 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) kmeans.fit(X) # 计算每个数据点的轮廓系数 score = silhouette_score(X, kmeans.labels_) # 计算整个聚类的 Silhouette 统计量 silhouette_scores.append(score) # 选择具有最大 Silhouette 统计量的 k 值 ...
kmeans = KMeans(n_clusters=n_clusters, init='k-means++', random_state=42, n_jobs=-1) ...
(n_clusters = k, n_jobs = 4, max_iter = iteration,random_state=1234)#分为k类,并发数414model.fit(data_zs)#开始聚类1516#简单打印结果17r1 = pd.Series(model.labels_).value_counts()#统计各个类别的数目18r2 = pd.DataFrame(model.cluster_centers_)#找出聚类中心19r = pd.concat([r2, r1]...
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...
'X','Y']]# 注意修正这里的语法错误# 初始化KMeans模型列表,并设定k的范围range_n_clusters = [1,2,3,4,5,6,7,8]# 扩大了k的范围inertia_scores = []# 对每个k值进行聚类并计算惯性指标forn_clustersinrange_n_clusters:# 使用KMeans算法kmeans = KMeans(n_clusters=n_clusters, random_state=42...
>>>from sklearn.clusterimportKMeans>>>importnumpyasnp>>>X=np.array([[1,2],[1,4],[1,0],...[10,2],[10,4],[10,0]])>>>kmeans=KMeans(n_clusters=2,random_state=0).fit(X)>>>kmeans.labels_array([1,1,1,0,0,0],dtype=int32)>>>kmeans.predict([[0,0],[12,3]])arra...