二維數字嵌入上的各種凝聚聚類?
在數字數據集的2D嵌入上用于聚集聚類的各種鏈接選項的說明。
此示例的目標是直觀地顯示指標的行為，而不是為數字找到好的聚類。這就是為什么這個例子適用于2D嵌入。
這個例子向我們展示的是聚集性聚類的“rich getting richer”的行為，這種行為往往會造成不均勻的聚類大小。這種行為對于平均鏈接策略來說是非常明顯的，它最終產生了幾個單點簇，而在單個鏈接中，我們得到了一個單一的中心簇，所有其他的簇都是從邊緣的噪聲點中提取出來的。
Computing embedding
Done.
ward : 0.42s
average : 0.44s
complete : 0.42s
single : 0.10s
# Authors: Gael Varoquaux
# License: BSD 3 clause (C) INRIA 2014
print(__doc__)
from time import time
import numpy as np
from scipy import ndimage
from matplotlib import pyplot as plt
from sklearn import manifold, datasets
X, y = datasets.load_digits(return_X_y=True)
n_samples, n_features = X.shape
np.random.seed(0)
def nudge_images(X, y):
# Having a larger dataset shows more clearly the behavior of the
# methods, but we multiply the size of the dataset only by 2, as the
# cost of the hierarchical clustering methods are strongly
# super-linear in n_samples
shift = lambda x: ndimage.shift(x.reshape((8, 8)),
.3 * np.random.normal(size=2),
mode='constant',
).ravel()
X = np.concatenate([X, np.apply_along_axis(shift, 1, X)])
Y = np.concatenate([y, y], axis=0)
return X, Y
X, y = nudge_images(X, y)
#----------------------------------------------------------------------
# Visualize the clustering
def plot_clustering(X_red, labels, title=None):
x_min, x_max = np.min(X_red, axis=0), np.max(X_red, axis=0)
X_red = (X_red - x_min) / (x_max - x_min)
plt.figure(figsize=(6, 4))
for i in range(X_red.shape[0]):
plt.text(X_red[i, 0], X_red[i, 1], str(y[i]),
color=plt.cm.nipy_spectral(labels[i] / 10.),
fontdict={'weight': 'bold', 'size': 9})
plt.xticks([])
plt.yticks([])
if title is not None:
plt.title(title, size=17)
plt.axis('off')
plt.tight_layout(rect=[0, 0.03, 1, 0.95])
#----------------------------------------------------------------------
# 2D embedding of the digits dataset
print("Computing embedding")
X_red = manifold.SpectralEmbedding(n_components=2).fit_transform(X)
print("Done.")
from sklearn.cluster import AgglomerativeClustering
for linkage in ('ward', 'average', 'complete', 'single'):
clustering = AgglomerativeClustering(linkage=linkage, n_clusters=10)
t0 = time()
clustering.fit(X_red)
print("%s :\t%.2fs" % (linkage, time() - t0))
plot_clustering(X_red, clustering.labels_, "%s linkage" % linkage)
plt.show()
腳本的總運行時間：(0分32.660秒)