March 2018
Beginner to intermediate
332 pages
6h 1m
Korean
Content preview from 9가지 사례로 익히는 고급 스파크 분석(2판): 현실 세계 빅데이터로 배우는 데이터 과학과 머신러닝
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151
5
장
K-평균 군집화로 네트워크 이상 탐지하기
colors = sapply(clusters, function(c) palette[c])
plot3d(projected_data, col = colors, size = 10)
시각화를 하려면
rgl
라이브러리와 그래픽을 지원하는 환경에서 알스튜디오를 실행해야 한다.
예를 들어,
macOS
사용자라면 애플 개발자 도구
Apple
Developer
Tools
의
X11
이 설치되어 있어야
한다.
시각화한 결과를 보면
3
차원 공간에 여러 데이터 포인트가 그려진 것을 알 수 있다 (그림
5
-
1
). 군집 번호에 따라 포인트들의 색이 다르지만, 많은 포인트가 서로 겹쳐 있고 결과가 빈
약해서 해석하기 어렵다. 어쨌든 이 시각화에서 두드러진 특징은 포인트들이 ‘
L
’자 형태로 배치
됐다는 점이다. 포인트들은
2
개의 특정 차원을 따라 변하면서 늘어선 듯 보이고 나머지 한 차
원의 변화는 미미하다.
그림
5-1
무작위
3
차원 투영
단 두 개의 특징이 다른 특징에 비해서 그 값의 범위가 훨씬 넓기 때문이라 이해된다. 대부
분의 특징이
0
~
1
의 값을 갖지만, 수신 바이트 수 (
bytes
-
sent
)와 송신 바이트 수 (
bytes
-
received
)는
0
에서 수만 사이의 다양한 값이 될 수 있다. 두 데이터 포인트의 유클리드 거리
가 이
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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.