# 使用肘部法确定k-means均值的k值

```import numpy as np
from sklearn.cluster import KMeans
from scipy.spatial.distance import cdist
import matplotlib.pyplot as plt

c1x = np.random.uniform(0.5, 1.5, (1, 10))
c1y = np.random.uniform(0.5, 1.5, (1, 10))
c2x = np.random.uniform(3.5, 4.5, (1, 10))
c2y = np.random.uniform(3.5, 4.5, (1, 10))
x = np.hstack((c1x, c2x))
y = np.hstack((c1y, c2y))
X = np.vstack((x, y)).T

K = range(1, 10)
meanDispersions = []
for k in K:
kmeans = KMeans(n_clusters=k)
kmeans.fit(X)
#理解为计算某个与其所属类聚中心的欧式距离
#最终是计算所有点与对应中心的距离的平方和的均值
meanDispersions.append(sum(np.min(cdist(X, kmeans.cluster_centers_, ‘euclidean‘), axis=1)) / X.shape[0])

plt.plot(K, meanDispersions, ‘bx-‘)
plt.xlabel(‘k‘)
plt.ylabel(‘Average Dispersion‘)
plt.title(‘Selecting k with the Elbow Method‘)
plt.show()```

X为：

```[[0.84223858 1.18059879]
[0.84834276 0.84499409]
[1.13263229 1.34316399]
[0.95487981 0.59743761]
[0.81646041 1.32361288]
[0.90405171 0.54047701]
[1.2723004  1.3461647 ]
[0.52939142 1.03325549]
[0.84592514 0.74344317]
[1.07882783 1.4286598 ]
[3.71702311 3.97510452]
[3.95476036 3.83842502]
[4.4297804  3.91854623]
[4.08686159 4.15798624]
[3.90406684 3.84413461]
[4.32395689 4.06825926]
[4.23112269 3.78578326]
[3.70602931 4.08608482]
[3.58690191 4.37072349]
[4.38564657 4.02168693]]```