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- from sklearn.datasets.samples_generator import make_blobs
- X, y_true = make_blobs(n_samples=300, centers=3,
- cluster_std=1.1, random_state=0)
- plt.scatter(X[:, 0], X[:, 1], s=50);
- from sklearn.cluster import KMeans
- kmeans = KMeans(n_clusters=3)
- kmeans.fit(X)
- y_kmeans = kmeans.predict(X)
- plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap='viridis')
- centers = kmeans.cluster_centers_
- plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200);
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