July 2019
Intermediate to advanced
365 pages
8h 13m
Chinese
Content preview from Python机器学习手册：从数据预处理到深度学习
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294
｜
第
19
章
聚类
#
预测观察值的分类
model.predict(new_observation)
array([0], dtype=int32)
这个观察值被预测为离某个分类的中心点距离最近的分类。可以使用
cluster_centers_
来查看这些中心点 ：
#
查看分类的中心点
model.cluster_centers_
array([[ 1.13597027, 0.09659843, 0.996271 , 1.01717187],
[-1.01457897, 0.84230679, -1.30487835, -1.25512862],
[-0.05021989, -0.88029181, 0.34753171, 0.28206327]])
延伸阅读
y
《
K-Means
聚类简介》（
DataScience.com
，
http://bit.ly/2Hjik1f
）
19.2
加速
K-Means
聚类
问题描述
要把观察值分成
k
个组，但是用
K-Means
算法需要太长的时间。
解决方案
使用
Mini-Batch K-Means
：
#
加载库
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import MiniBatchKMeans
#
加载数据
iris = datasets.load_iris()
features = iris.data ...
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