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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252
｜
第
7
章
2.
对所有成对的两个簇
k
和
l
，计算出它们之间的相异度
D
(
C
k
,
C
l
)
。
3.
将相异度
D
(
C
k
,
C
l
)
最小的两个簇
C
k
和
C
l
合并。
4.
如果余下的簇多于
1
个，回到第
2
步，否则算法结束。
7.3.4
测量相异度
常用的相异度测量方法有四种：完全距离法、单距离法（
single linkage
）、平均距离法
（
average
linkage
）和最小方差法。多数层次聚类软件（包括
hclust
和
linkage
）支持这些
方法（以及其他方法）。上一节介绍的完全距离法往往生成成员相似的簇。单距离法使用
两个簇记录之间的最小距离作为相异度：
D
(
A
,
B
)
=
min
d
(
a
i
,
b
j
)
这是一种“贪婪”方法，它生成的簇可以包含差异非常大的元素。平均距离法使用所有记
录对之间的平均距离，是单距离法和完全距离法的一种折中。最后，最小方差法也称为
Ward
距离法，它与
K-
均值类似，因为它最小化了簇内平方和（参见
7.2
节
）。
在图
7-9
中
，在埃克森美孚和雪佛龙的股票收益上使用四种方法进行了层次聚类，对于每
种测量方法，都保留四个簇。
图 7-9：股票数据上四种相异度测量的比较
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