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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19.3
使用
Meanshift
聚类算法
｜
295
cluster = MiniBatchKMeans(n_clusters=3, random_state=0, batch_size=100)
#
训练模型
model = cluster.fit(features_std)
讨论
Mini-Batch K-Means
和我们在
19.1
节讨论的
K-Means
算法的工作原理类似。如果不深
究过多细节，那么这两种算法的区别就是，前者计算量最大的步骤只在观察值的一部分
随机样本上而非所有的观察值上执行。这个方法可以在只损失一小部分质量的情况下显
著缩短算法收敛的时间。
MiniBatchKMeans
的用法和
KMeans
十分相似，最大的区别在于
batch_size
参数。
batch_size
控制每个批次中随机选择的观察值的数量。批次中的观察值越多，在训练过
程中需要花费的算力就越大。
19.3
使用
Meanshift
聚类算法
问题描述
在不对分类的数量和形状做假设的情况下对观察值聚类。
解决方案
使用
MeanShift
聚类 ：
#
加载库
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import MeanShift
#
加载数据
iris = datasets.load_iris()
features = iris.data
#
标准化特征
scaler = ...
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