May 2021
Beginner to intermediate
380 pages
9h 22m
Korean
Content preview from 데이터 과학을 위한 통계(2판)
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348
데이터 과학을 위한 통계
dxyxyx
yx
y
pp
,
()
=−
()
+−
()
++ −
()
11
2
22
2
2
이제 클러스터 간의 거리를 알아보자.
A
= (
a
1
,
a
2
, … ,
a
m
)과
B
= (
b
1
,
b
2
, … ,
b
q
)라는 두
개의 클러스터
A
와
B
를 고려하자. 클러스터
A
의 구성원들과
B
의 구성원들 사이의 거리를 사
용하여 클러스터 간의
D
(
A
,
B
) 비유사도를 측정할 수 있다.
비유사도를 측정하는 한 가지 방법은
A
와
B
사이의 모든 레코드 쌍의 최대 거리를 사용하는
완
전연결
complete
linkage
방식이다.
DA
Bd
ab
ij
,max ,
()
=
()
모든
i
,
j
쌍에 대해
이것은 비유사도를 모든 쌍 사이의 가장 큰 차이로 정의한다.
병합 알고리즘의 주요 단계는 다음과 같다.
1.
데이터의 모든 레코드에 대해, 단일 레코드로만 구성된 클러스터들로 초기 클러스터 집
합을 만든다.
2.
모든 쌍의 클러스터
k
,
l
사이의 비유사도
D
(
C
k
,
C
l
)을 계산한다.
3.
D
(
C
k
,
C
l
)에 따라 가장 가까운 두 클러스터
C
k
와
C
l
을 병합한다.
4.
둘 이상의 클러스터가 남아 있으면
2
단계로 다시 돌아간다. 그렇지 않고 클러스터가 하
나 남는다면 알고리즘을 멈춘다.
7.3.4
비유사도 측정
비유사도를 측정하는 네 가지 일반적인 지표는
완전연결
,
단일연결
single
linkage
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
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.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.