使用Sklearn实现K-means

import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.cluster import KMeans
iris = datasets.load_iris() 
X = iris.data[:, :4] # #表示我们取特征空间中的4个维度
print(X.shape)
 
# 绘制数据分布图
#plt.figure(figsize=(15,8),dpi=80)
plt.scatter(X[:, 0], X[:, 1], c="red", marker='*', label='see') 
plt.xlabel('sepal length') 
plt.ylabel('sepal width') 
plt.legend(loc=2) 
plt.show() 
 
estimator = KMeans(n_clusters=3) # 构造聚类器
estimator.fit(X) # 聚类
label_pred = estimator.labels_ # 获取聚类标签
# 绘制k-means结果
x0 = X[label_pred == 0]
x1 = X[label_pred == 1]
x2 = X[label_pred == 2]
plt.figure(figsize=(15,8),dpi=80)
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('sepal length') 
plt.ylabel('sepal width') 
plt.legend(loc=2) 
plt.show()