K-Means聚类可视化

import numpy as np
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
np.random.seed(2)
X = np.random.randn(50, 2)
X[:25, 0] += 3
X[:25, 1] -= 4
kmeans = KMeans(n_clusters=2, n_init=20, random_state=1)
clusters = kmeans.fit_predict(X)
centers = kmeans.cluster_centers_
plt.figure(figsize=(10, 6))
plt.scatter(X[:, 0], X[:, 1], c=clusters, cmap='coolwarm', s=50)
plt.scatter(centers[:, 0], centers[:, 1], marker='*', c='red', s=200, label='聚类中心')
plt.xlabel('X1')
plt.ylabel('X2')
plt.title('K-Means聚类结果')
plt.legend()
plt.show()