本文轉載自劉建平

????建立數據

import numpy as np import matplotlib.pyplot as plt #%matplotlib inline from sklearn.datasets.samples_generator import make_blobs # X為樣本特征，Y為樣本簇類別， 共1000個樣本，每個樣本4個特征，共4個簇，簇中心在[-1,-1], [0,0],[1,1], [2,2]， 簇方差分別為[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類

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評估的聚類分數

from sklearn import metrics metrics.calinski_harabaz_score(X, y_pred)Out[52]: 3116.1706763322227

????分3類

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評估的聚類分數

from sklearn import metrics metrics.calinski_harabaz_score(X, y_pred)Out[54]: 2931.625030199556

????分4類

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評估的聚類分數

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

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