October 2021
Intermediate to advanced
289 pages
8h 31m
Chinese
Content preview from 数据科学中的实用统计学(第2版)
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无监督学习
｜
259
扩展到大数据上。最后，这种算法非常复杂，与其他方法相比，它更加难以掌握。
本节要点
•
簇被假定为从多个具有不同概率分布的数据生成过程中产生的。
•
假定有不同数目的分布（通常是正态分布），需要拟合多个不同的模型。
•
这种方法会选择那种不需要使用太多参数（即过拟合）就能很好地拟合数据的模型
（以及相关的簇数目）。
7.4.4
扩展阅读
如果想了解基于模型的聚类的更多细节，可以参考
mclust
和
GaussianMixture
的文档。
7.5
数据缩放与分类变量
无监督学习技术通常需要对数据进行适当的缩放，这与多数回归和分类技术不同，在回归
与分类中，数据缩放并不重要（
KNN
是一种例外，参见
6.1
节
）。
本节关键术语
缩放
对数据进行挤压或扩展，通常使多个变量处于同一数量级。
归一化
一种缩放方法——先减去均值，再除以标准差。
同义词
标准化
Gower
距离
一种应用于数值型和分类型数据的混合数据的缩放算法，它把所有变量都转换到
0
和
1
之间。
举例来说，在个人贷款数据中，变量的单位和数量级都差别非常大。有些变量的值比较小
（如工作年限），而另一些变量的值则非常大（如以美元为单位的贷款额）。如果不进行数
据缩放，那么
PCA
、
K-
均值和其他聚类方法都会被数值大的变量所主导
，忽略那些数值较
小的变量。
分类数据会在某些聚类过程中造成特殊的问题。在
KNN
中
，无序的因子变量通常使用独
热编码（参见
6.1.3
节）
转换为一组二元变量（
0/1
）。二元变量很可能与其他数据不在一个
数量级上，而且因为它只有两个值，所以在
PCA
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.