聚类可视化分析

import matplotlib.pyplot as plt
import numpy as np
from sklearn.cluster import AffinityPropagation
from sklearn.datasets import make_blobs

centers = [[1, 1], [-1, -1], [1, -1]]
data, labels_true = make_blobs(n_samples=100, centers=centers, cluster_std=0.5,
random_state=0)

for id, label in enumerate(labels_true):
color = plt.cm.Set1(label)
plt.scatter(data[id][0], data[id][1], color = color,marker='o',s=4)
plt.show()

clustering = AffinityPropagation(damping=0.9, max_iter=100).fit(data)
for id, label in enumerate(clustering.labels_):
color = plt.cm.Set1(label)
plt.scatter(data[id][0], data[id][1], color = color,marker='o',s=4)
plt.show()