用scikit-learn学习K-Means聚类

本文转载自刘建平

    建立数据

import numpy as np
import matplotlib.pyplot as plt
#%matplotlib inline
from sklearn.datasets.samples_generator import make_blobs
# X为样本特征，Y为样本簇类别， 共1000个样本，每个样本4个特征，共4个簇，簇中心在[-1,-1], [0,0],[1,1], [2,2]， 簇方差分别为[0.4, 0.2, 0.2]
X, y = make_blobs(n_samples=1000, n_features=2, centers=[[-1,-1], [0,0], [1,1], [2,2]], cluster_std=[0.4, 0.2, 0.2, 0.2], 
                  random_state =9)
plt.scatter(X[:, 0], X[:, 1], marker='o')
plt.show()

    分2类

from sklearn.cluster import KMeans
y_pred = KMeans(n_clusters=2, random_state=9).fit_predict(X)
plt.scatter(X[:, 0], X[:, 1], c=y_pred)
plt.show()

    Calinski-Harabasz Index评估的聚类分数

from sklearn import metrics
metrics.calinski_harabaz_score(X, y_pred)

Out[52]: 3116.1706763322227

    分3类

from sklearn.cluster import KMeans
y_pred = KMeans(n_clusters=3, random_state=9).fit_predict(X)
plt.scatter(X[:, 0], X[:, 1], c=y_pred)
plt.show()

    Calinski-Harabasz Index评估的聚类分数

from sklearn import metrics
metrics.calinski_harabaz_score(X, y_pred)

Out[54]: 2931.625030199556

    分4类

from sklearn.cluster import KMeans
y_pred = KMeans(n_clusters=4, random_state=9).fit_predict(X)
plt.scatter(X[:, 0], X[:, 1], c=y_pred)
plt.show()

    Calinski-Harabasz Index评估的聚类分数

metrics.calinski_harabaz_score(X, y_pred)
Out[56]: 5924.050613480169