擅长:python、mysql、java
<p>我想你需要为每个集群编号调用kmeans fit。我合并了来自<a href="http://scikit-learn.org/stable/modules/clustering.html#calinski-harabaz-index" rel="nofollow noreferrer">scikit learn documentation</a>的示例和下面的代码。在</p>
<pre><code>from sklearn import metrics
from sklearn.metrics import pairwise_distances
from sklearn import datasets
dataset = datasets.load_iris()
X = dataset.data
y = dataset.target
import numpy as np
from sklearn.cluster import KMeans
kmeans_model = KMeans(n_clusters=3, random_state=1).fit(X)
labels = kmeans_model.labels_
metrics.calinski_harabaz_score(X, labels)
for k in range(2, 21):
kmeans_model = KMeans(n_clusters=k, random_state=1).fit(X)
labels = kmeans_model.labels_
labels = kmeans_model.labels_
print k, metrics.calinski_harabaz_score(X, labels)
</code></pre>
<p>输出低于。在</p>
^{2}$
<p>根据这个结果,3个聚类中心是最好的3560.399924247英寸。在</p>