层次聚类切分

import numpy as np
from sklearn.preprocessing import StandardScaler
import scipy.cluster.hierarchy as shc
from scipy.cluster.hierarchy import fcluster
np.random.seed(2)
X = np.random.randn(50, 2)
X[:25, 0] += 3
X[:25, 1] -= 4
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
Z = shc.linkage(X_scaled, method='ward')
labels_2 = fcluster(Z, t=7, criterion='distance')
labels_3 = fcluster(Z, t=5, criterion='distance')
print('切分高度7时的聚类数:', len(np.unique(labels_2)))
print('切分高度5时的聚类数:', len(np.unique(labels_3)))