第14章 聚类方法 - 寻找 K 值 - 可视化

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

data = np.random.rand(100, 2)
inertias = []
K_range = range(1, 10)
for k in K_range:
    kmeans = KMeans(n_clusters=k, random_state=0).fit(data)
    inertias.append(kmeans.inertia_)

plt.plot(K_range, inertias, 'bx-')
plt.xlabel('k')
plt.ylabel('Inertia')
plt.title('Elbow Method For Optimal k')