机器学习（聚类十一）——不同聚类算法在不同数据分布情况下的聚类效果
版权申诉
53 浏览量
2020-12-21
11:28:07
上传
评论
收藏 432KB PDF 举报
机器学习（聚类十一）机器学习（聚类十一）——不同聚类算法在不同数据分布情况下的聚类效果不同聚类算法在不同数据分布情况下的聚类效果
至此聚类相关的内容告一段落，前面十篇博客介绍了常见的几种聚类算法，也加入了一些代码实现。这篇博客来一个汇总的实例，分别创建圆形数据、月牙形数据、聚团
数据以及随机数据，并测试不同数据在各种不同聚类算法中的聚类效果以及消耗时间。
import time
import warnings
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from sklearn import cluster
from sklearn import datasets as ds
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import StandardScaler
## 设置属性防止中文乱码
mpl.rcParams['font.sans-serif'] = [u'SimHei'] mpl.rcParams['axes.unicode_minus'] = False
warnings.filterwarnings(action='ignore', category=UserWarning)
## 产生模拟数据
n_samples = 1500
np.random.seed(0)
#产生圆形的
noisy_circles = ds.make_circles(n_samples=n_samples, factor=.5, noise=.05)
#产生月牙形
noisy_moons = ds.make_moons(n_samples=n_samples, noise=.05)
#高斯分布
blobs = ds.make_blobs(n_samples=n_samples, n_features=2, cluster_std=0.5, centers=3, random_state=0)
no_structure = np.random.rand(n_samples, 2), None
datasets = [noisy_circles, noisy_moons, blobs, no_structure] clusters = [2,2,3,2]
clustering_names = [
'KMeans', 'MiniBatchKMeans', 'AC-ward', 'AC-average',
'Birch', 'DBSCAN','SpectralClustering']
## 开始画图
plt.figure(figsize=(len(clustering_names) * 2 + 3, 9.5), facecolor='w')
plt.subplots_adjust(left=.02, right=.98, bottom=.001, top=.96, wspace=.05,hspace=.01)
colors = np.array([x for x in 'bgrcmykbgrcmykbgrcmykbgrcmyk'])
colors = np.hstack([colors] * 20)
plot_num = 1
for i_dataset,(dataset, n_cluster) in enumerate(zip(datasets, clusters)):
X,y = dataset
X = StandardScaler().fit_transform(X)
connectivity = kneighbors_graph(X, n_neighbors=10, include_self=False)
connectivity = 0.5 * (connectivity + connectivity.T)
km = cluster.KMeans(n_clusters=n_cluster)
mbkm = cluster.MiniBatchKMeans(n_clusters=n_cluster)
ward = cluster.AgglomerativeClustering(n_clusters=n_cluster,connectivity=connectivity, linkage='ward')
average = cluster.AgglomerativeClustering(n_clusters=n_cluster,connectivity=connectivity, linkage='average')
birch = cluster.Birch(n_clusters=n_cluster)
dbscan = cluster.DBSCAN(eps=.2)
spectral = cluster.SpectralClustering(n_clusters=n_cluster, eigen_solver='arpack', affinity="nearest_neighbors")
clustering_algorithms = [km, mbkm, ward, average, birch, dbscan, spectral]
for name, algorithm in zip(clustering_names, clustering_algorithms):
t0 = time.time()
algorithm.fit(X)
t1 = time.time()
# 如果模型中存在"labels__"这个属性的话，那么获取这个预测的类别值
if hasattr(algorithm, 'labels_'):
y_pred = algorithm.labels_.astype(np.int)
else:
y_pred = algorithm.predict(X)
# 画子图
plt.subplot(4, len(clustering_algorithms), plot_num)
if i_dataset == 0:
plt.title(name, size=18)
plt.scatter(X[:, 0], X[:, 1], color=colors[y_pred].tolist(), s=10)
# 如果模型有中心点属性，那么画出中心点
if hasattr(algorithm, 'cluster_centers_'):
centers = algorithm.cluster_centers_
center_colors = colors[:len(centers)] plt.scatter(centers[:, 0], centers[:, 1], s=100, c=center_colors)
plt.xlim(-2, 2)
plt.ylim(-2, 2)
plt.xticks(())
plt.yticks(())
plt.text(.99, .01, ('%2fs' % (t1 - t0)).lstrip('0'),
transform=plt.gca().transAxes, size=15,
horizontalalignment='right')
plot_num += 1
plt.show()
最后看一下运行效果
资源评论
