【sklearn入门】通过sklearn实现k-means并可视化聚类结果

 import numpy as np
 from sklearn.cluster import KMeans
 from mpl_toolkits.mplot3d import Axes3D
 import matplotlib.pyplot as plt

 data = np.random.rand(100, 3)  # 生成一个随机数据，样本大小为100, 特征数为3

 estimator = KMeans(n_clusters=3)  # 构造聚类器

 y = estimator.fit_predict(data)  # 聚类

 label_pred = estimator.labels_  # 获取聚类标签

 centroids = estimator.cluster_centers_  # 获取聚类中心

 print(label_pred)
 print(centroids)

 fig = plt.figure()
 ax = Axes3D(fig)
 ax.scatter(data[:, 0], data[:, 1], data[:, 2], c=y, marker='*')

 ax.scatter(centroids[:, 0], centroids[:, 1], centroids[:, 2], marker='>')
 plt.axis([0, 1, 0, 1])
 plt.show()

显示效果如下：