October 2018
Beginner to intermediate
238 pages
6h 32m
Chinese
Content preview from 面向数据科学家的实用统计学
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无监督学习
｜
205
层次聚类的灵活性是有一定代价的，它不能很好地扩展到具有数百万条记录的大规模数据
集上。即便是只有数万条记录的中等规模数据集，层次聚类可能也需要大量的计算资源。
事实上，层次聚类的大部分应用都集中在一些规模相对较小的数据集上。
7.3.1
一个简单的例子
我们将层次聚类应用于一个具有
n
条记录和
p
个变量的数据集。其中，我们使用了两个基
本度量。
•
距离度量
d
i
,
j
测量两个记录
i
和
j
之间距离。
•
相异性度量
D
A
,
B
，基于每个类内成员间的距离
d
i
,
j
，测量两个类
A
和
B
间的差异。
对于使用数值型数据的应用，关键在于如何选择相异性度量。层次聚类首先使每个记录独
自构成一个类，然后迭代地合并相异性最低的类。
在
R
语言中，可以使用 hclust 函数执行层次聚类。hclust 函数和 kmeans 函数的一大区
别是，它并非运行在数据本身之上，而是运行于成对记录的距离
d
i
,
j
之上。我们可以使用
dist 函数分别计算所有数据对间的距离。例如，下面的代码对一组公司的股票收益数据应
用层次聚类。
syms1 <- c('GOOGL', 'AMZN', 'AAPL', 'MSFT', 'CSCO', 'INTC', 'CVX',
'XOM', 'SLB', 'COP', 'JPM', 'WFC', 'USB', 'AXP',
'WMT', 'TGT', 'HD', 'COST')
# 下面执行转置操作。因为要按照公司聚类，所以需要股票数据按行排列
df <- t(sp500_px[row.names(sp500_px)>='2011-01-01', ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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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.