kmeans聚类算法树状图python实现

要使用K-means算法实现聚类树状图，可以按照以下步骤进行：

1. 导入所需的库：

import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from scipy.cluster.hierarchy import dendrogram, linkage

2. 准备数据集：

X = np.array([[1, 2], [1, 4], [1, 0], [4, 2], [4, 4], [4, 0]])

3. 使用K-means算法进行聚类：

kmeans = KMeans(n_clusters=2)
means.fit(X)

4. 获取聚类标签：

labels = kmeans.labels_

5. 使用层次聚类算法生成树状图：

Z = linkage(X, method='single')
dn = dendrogram(Z)
plt.show()

完整代码如下：

import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from scipy.cluster.hierarchy import dendrogram, linkage

X = np.array([[1, 2], [1, 4], [1, 0], [4, 2], [4, 4], [4, 0]])

kmeans = KMeans(n_clusters=2)
kmeans.fit(X)

labels = kmeans.labels_

Z = linkage(X, method='single')
dn = dendrogram(Z)
plt.show()