程式碼和結果：

import matplotlib.pyplot as plt  
import numpy as np  
from sklearn.cluster import KMeans
from sklearn import datasets  
X = iris.data[:, 2:4] ##表示我們只取特徵空間中的後兩個維度
print(X.shape)
#繪製資料分佈圖
plt.scatter(X[:, 0], X[:, 1], c = "red", marker='o', label='see')  
plt.xlabel('petal length')  
plt.ylabel('petal width'
 
)  
plt.legend(loc=2)  
plt.show()  

estimator = KMeans(n_clusters=3)#構造聚類器
estimator.fit(X)#聚類
label_pred = estimator.labels_ #獲取聚類標籤
#繪製k-means結果
x0 = X[label_pred == 0]
x1 = X[label_pred == 1]
x2 = X[label_pred == 2]
plt.scatter(x0[:, 0], x0[:, 1], c = "red", marker='o', label='label0')  
plt.scatter(x1[:, 0
 
], x1[:, 1], c = "green", marker='*', label='label1')  
plt.scatter(x2[:, 0], x2[:, 1], c = "blue", marker='+', label='label2')  
plt.xlabel('petal length')  
plt.ylabel('petal width')  
plt.legend(loc=2)  
plt.show()  

光看這2個特徵，那聚類算是非常完美了