肘部观察平均距离与类簇数量的关系 （Python 机器学习及实践）

# -*- coding: utf-8 -*-
import numpy as np
from sklearn.cluster import KMeans
from scipy.spatial.distance import cdist
import matplotlib.pyplot as plt

cluster1 = np.random.uniform(0.5, 1.5, (2, 10))
cluster2 = np.random.uniform(5.5, 6.5, (2, 10))
cluster3 = np.random.uniform(3.0, 4.0, (2, 10))

X = np.hstack((cluster1, cluster2, cluster3)).T
plt.scatter(X[:, 0], X[:, 1])
plt.xlabel('x1')
plt.ylabel('x2')
plt.title('data')
plt.show()

K = range(1, 10)
meandistortions = []
for k in K:
    kmeans = KMeans(n_clusters=k)
    kmeans.fit(X)
    meandistortions.append(sum(np.min(cdist(X, kmeans.cluster_centers_, 'euclidean'), axis=1)) / X.shape[0])

plt.plot(K, meandistortions, 'bx-')
plt.xlabel('k')
plt.ylabel('Average Dispersion')
plt.title('Selecting k with the Elbow method')

plt.show()