KMeans 聚类算法实现：Python 代码示例及可视化

from sklearn.cluster import KMeans
from matplotlib import pyplot
import numpy as np

# 把上面数据点分为两组（非监督学习）
clf = KMeans(n_clusters=2, random_state=0)
x = np.array([[1, 2], [1.5, 1.8], [5, 8], [8, 8], [1, 0.6], [9, 11]])
clf.fit(x)  # 分组
########## Begin ##########
# 两组数据点的中心点
centers = clf.cluster_centers_
# 每个数据点所属分组
labels = clf.labels_
########## End ##########
print(centers)
print(labels)

for i in range(len(labels)):
    pyplot.scatter(x[i][0], x[i][1], c=('r' if labels[i] == 0 else 'b'))
pyplot.scatter(centers[:, 0], centers[:, 1], marker='*', s=100)

# 预测
predict = [[2, 1], [6, 9]]
label = clf.predict(predict)
for i in range(len(label)):
    pyplot.scatter(predict[i][0], predict[i][1], c=('r' if label[i] == 0 else 'b'), marker='x')

pyplot.show()
pyplot.savefig('/data/workspace/myshixun/step3/pictrue1/pic1.jpg')