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<pre class="snippet-code-html lang-html prettyprint-override"><code>from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_samples, silhouette_score
X, y = make_blobs(n_samples=500,
n_features=2,
centers=4,
cluster_std=1,
center_box=(-10.0, 10.0),
shuffle=True,
random_state=1)
sil=[]
#start the cluster range from 2
range_n_clusters = range(2,10)
for n_clusters in range_n_clusters:
clusterer = KMeans(n_clusters=n_clusters, random_state=10)
cluster_labels = clusterer.fit_predict(X)
silhouette_avg = silhouette_score(X, cluster_labels)
print("For n_clusters =", n_clusters,
"The average silhouette_score is :", silhouette_avg)
sil.append(silhouette_avg)</code></pre>
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<p>这是一个应用于随机样本的Kmeans聚类的例子,并根据轮廓分数找到最佳聚类。我认为这将有助于你或请提供更多的信息
<a href="https://i.stack.imgur.com/ARzX4.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/ARzX4.png" alt="enter image description here"/></a></p>