利用AP算法进行聚类：
首先导入需要的包：
from sklearn.cluster import AffinityPropagation from sklearn import metrics from sklearn.datasets.samples_generator import make_blobs
生成一组数据：
centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(n_samples=300, centers=centers, cluster_std=0.5, random_state=0)
以上代码包括3个类簇的中心点以及300个以这3个点为中心的样本点。
接下来要利用AP算法对这300个点进行聚类。
af = AffinityPropagation(preference=-50).fit(X) # preference采用负的欧氏距离 cluster_centers_indices = af.cluster_centers_indices_ labels = af.labels_ # 样本标签 n_clusters_ = len(cluster_centers_indices) # 类簇数
打印各种评价指标分数：
print('估计的类簇数: %d' % n_clusters_) print('Homogeneity: %0.3f' % metrics.homogeneity_score(labels_true, labels)) print('Completeness: %0.3f' %metrics.completeness_score(labels_true, labels)) print('V-measure: %0.3f' %metrics.v_measure_score(labels_true, labels)) print('Adjusted Rand Index:%0.3f' %metrics.adjusted_rand_score(labels_true, labels)) print('Adjusted Mutual Information:%0.3f'%metrics.adjusted_mutual_info_score(labels_true, labels)) print('Silhouette Coefficient:%0.3f' %metrics.silhouette_score(X, labels, metric='sqeuclidean')) # sqeuclidean欧式距离平方
可视化聚类结果：
导入画图需要的包：
import matplotlib.pyplot as plt from itertools import cycle plt.close('all') plt.figure(1) plt.clf() # 清除当前图的所有信息 colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')
close()方法介绍【可忽略】
close方法简介：
matplotlib.pyplot.close(*args) --- Close a figure window.
close() by itself closes the current figure
close(fig) closes the Figure instance fig
close(num) closes the figure number num
close(name) where name is a string, closes figure with that label
close('all') closes all the figure windows