5회 추천아 놀자 방송 방송 자료 K-means를 이용하여 Personal Analytics를 구현해 봅니다.

1. 추천아 놀자 5회
무엇이든 군집화하기
( K-means 좀더)
곧 시작함

2. RescueTime 에 대하여
자신의 PC의 App, 웹사이트 등 사용시간을 기록하여
카테고리를 분류하여 생산성을 측정해 주는 도구

3. RescueTime 에 대하여
갑자기 왜?

4. 오늘 분류할 데이터 셋이 내 PC의 APP 사용 시간을
기록한 데이터 입니다.
RescueTime 에 대하여

5. 좀더 알아 봅시다.
http://rescuetime.com 무료 버젼
RescueTime 에 대하여

6. 우리가 사용한 데이터 셋
PC App별 사용 시간 측정( 초단위 )

7. 우리가 사용한 데이터 셋
App의 카테고리 분류?
: PC 프로세서 이름 또는 타이틀별 분류표에 의해 분류
개발
- Eclipse
- SQLiteExpertPer
s.exe
- mstsc.exe
- devenv.exe
- ttermpro.exe
- wireshark.exe
- MySQLWorkbench.
exe
기타 등등
문서
- EDITPLUS.EXE
- EXCEL.EXE
- Hwp.exe
- NOTEPAD.EXE
- POWERPNT.EXE
- PaintDotNet.exe
- VISIO.EXE
- WINWORD.EXE
- Evernote.exe
기타 등등
인터넷
- chrome.exe
- iexplore.exe
- firefox.exe
- Windows
Internet
Explorer
기타 등등
PC운영
- ALSong.exe
- ALZip.exe
- Setup.exe
- calc.exe
- Explorer.EXE
- 시작 메뉴
- Program Manager
기타 등등

8. 우리가 사용할 데이터 셋
2014/05/01~ 05/31 기간의 내
Office-PC와 Home-PC의 PC App의 사용 시간

9. 우리가 사용한 데이터 셋
일자별로 카테고리 분류별로 사용 시간 (초) 측정
레알!!! 실제 데이터

10. 이제 이것으로 무엇을 하나?
Office-PC 데이터끼리
Home-PC 데이터끼리
데이터군집화를해보자

11. 이제 이것으로 무엇을 하나?
어떻게??
K-Means 군집화 알고리즘!!

12. 주어진데이터를K개의군집으로나누는알고리즘이다.
①나눌군집개수K를결정
②임의의군집중심으로가까운점들끼리묶음
③각각의군집에대하여평균을새로구함
④새로운평균의중심값으로가장근접한점들끼리묶음
⑤3번,4번단계를반복적으로수행하여변경이없을때까지수행
① ② ③ ④
⑤
K-Means 군집화

13. 유사도 측정
행 레이블 PC운영(초) 미분류(초) 개발(초) 기타업무(초) 문서(초) 인터넷(초) 총합계(초)
20140513-OFFICE 90 1775 15760 2160 8570 9315 37670
20140513-HOME-PC 415 4015 5 6125 10560
20140514-OFFICE 235 1130 10090 5115 11745 13420 41735
20140514-HOME-PC 25 1115 760 10 1105 3015
20140513-OFFICE 20140513-HOME-PC 20140514-OFFICE 20140514-HOME-PC
20140513-OFFICE 1.0 0.8386 0.9826 0.8516
20140513-HOME-PC 0.8386 1.0 0.8771 0.9596
20140514-OFFICE 0.9826 0.8771 1.0 0.8918
20140514-HOME-PC 0.8516 0.9596 0.8918 1.0
Cosine Similarity 로 유사도 측정

14. 유사도 측정
행 레이블 PC운영(초) 미분류(초) 개발(초) 기타업무(초) 문서(초) 인터넷(초) 총합계(초)
20140513-OFFICE 90 1775 15760 2160 8570 9315 37670
20140513-HOME-PC 415 4015 5 6125 10560
20140514-OFFICE 235 1130 10090 5115 11745 13420 41735
20140514-HOME-PC 25 1115 760 10 1105 3015
20140513-OFFICE 20140513-HOME-PC 20140514-OFFICE 20140514-HOME-PC
20140513-OFFICE 1.0 547.1937 154.0941 665.6763
20140513-HOME-PC 547.1937 1.0 601.2171 159.5431
20140514-OFFICE 154.0941 601.2171 1.0
729.1036
20140514-HOME-PC 665.6763 159.5431 729.1036 1.0
Euclidean로 유사도 측정

15. K-Means로 군집화 하기
K-Means 과정
- 클러스터링 개수 설정
2개

16. K-Means로 군집화 하기

17. K-Means로 군집화 하기
Cluster 1
[1.0, 111.0, 242.0, 11.0, 65.0, 140.0, 570.0, 20140507-CHOIKYUMIN]
[9.0, 38.0, 127.0, 68.0, 14.0, 123.0, 378.0, 20140508-CHOIKYUMIN]
[0.0, 0.0, 0.0, 0.0, 0.0, 20.0, 20.0, 20140508-HANULMIN-PC]
[4.0, 43.0, 20.0, 27.0, 54.0, 404.0, 552.0, 20140509-CHOIKYUMIN]
[3.0, 29.0, 293.0, 51.0, 20.0, 142.0, 538.0, 20140512-CHOIKYUMIN]
[0.0, 2.0, 0.0, 0.0, 33.0, 39.0, 74.0, 20140512-HANULMIN-PC]
[2.0, 30.0, 263.0, 36.0, 143.0, 155.0, 628.0, 20140513-CHOIKYUMIN]
[4.0, 19.0, 168.0, 85.0, 196.0, 224.0, 696.0, 20140514-CHOIKYUMIN]
[2.0, 46.0, 262.0, 49.0, 25.0, 117.0, 502.0, 20140515-CHOIKYUMIN]
[1.0, 23.0, 241.0, 17.0, 11.0, 190.0, 484.0, 20140516-CHOIKYUMIN]
[0.0, 15.0, 0.0, 0.0, 1.0, 88.0, 104.0, 20140518-HANULMIN-PC]
[2.0, 48.0, 226.0, 29.0, 31.0, 139.0, 475.0, 20140519-CHOIKYUMIN]
[1.0, 24.0, 162.0, 39.0, 11.0, 170.0, 408.0, 20140520-CHOIKYUMIN]
[0.0, 1.0, 0.0, 0.0, 1.0, 48.0, 49.0, 20140520-HANULMIN-PC]
[7.0, 19.0, 41.0, 46.0, 2.0, 37.0, 151.0, 20140521-CHOIKYUMIN]
[8.0, 21.0, 250.0, 17.0, 5.0, 208.0, 509.0, 20140523-CHOIKYUMIN]
[5.0, 61.0, 302.0, 45.0, 22.0, 209.0, 644.0, 20140526-CHOIKYUMIN]
[2.0, 100.0, 117.0, 0.0, 23.0, 72.0, 315.0, 20140526-HANULMIN-PC]
[2.0, 17.0, 167.0, 50.0, 63.0, 220.0, 519.0, 20140527-CHOIKYUMIN]
[12.0, 3.0, 0.0, 0.0, 0.0, 43.0, 58.0, 20140527-HANULMIN-PC]
[1.0, 22.0, 195.0, 61.0, 15.0, 293.0, 587.0, 20140528-CHOIKYUMIN]
[3.0, 23.0, 169.0, 48.0, 17.0, 170.0, 431.0, 20140529-CHOIKYUMIN]
[8.0, 27.0, 148.0, 31.0, 111.0, 265.0, 590.0, 20140530-CHOIKYUMIN]
[2.0, 0.0, 12.0, 17.0, 0.0, 22.0, 54.0, 20140530-HANULMIN-PC]
[0.0, 0.0, 0.0, 0.0, 0.0, 14.0, 14.0, 20140531-HANULMIN-PC]
Cluster 2
[0.0, 48.0, 0.0, 0.0, 0.0, 2.0, 51.0, 20140503-HANULMIN-PC]
[0.0, 150.0, 0.0, 0.0, 0.0, 22.0, 172.0, 20140504-HANULMIN-PC]
[1.0, 92.0, 6.0, 10.0, 5.0, 46.0, 160.0, 20140509-HANULMIN-PC]
[7.0, 67.0, 0.0, 0.0, 0.0, 102.0, 176.0, 20140513-HANULMIN-PC]
[0.0, 19.0, 0.0, 13.0, 0.0, 18.0, 50.0, 20140514-HANULMIN-PC]
[3.0, 61.0, 0.0, 0.0, 88.0, 90.0, 242.0, 20140515-HANULMIN-PC]
[0.0, 15.0, 0.0, 0.0, 0.0, 0.0, 15.0, 20140517-CHOIKYUMIN]
[0.0, 11.0, 0.0, 0.0, 0.0, 0.0, 11.0, 20140518-CHOIKYUMIN]
[6.0, 163.0, 8.0, 0.0, 8.0, 141.0, 327.0, 20140519-HANULMIN-PC]
[0.0, 135.0, 0.0, 0.0, 0.0, 47.0, 182.0, 20140521-HANULMIN-PC]
[1.0, 193.0, 0.0, 0.0, 0.0, 60.0, 254.0, 20140522-HANULMIN-PC]
[2.0, 62.0, 0.0, 14.0, 0.0, 59.0, 137.0, 20140523-HANULMIN-PC]
[0.0, 5.0, 0.0, 0.0, 0.0, 0.0, 5.0, 20140524-CHOIKYUMIN]
[0.0, 13.0, 0.0, 0.0, 0.0, 0.0, 13.0, 20140525-CHOIKYUMIN]
[7.0, 61.0, 14.0, 0.0, 6.0, 166.0, 254.0, 20140525-HANULMIN-PC]
[5.0, 128.0, 0.0, 12.0, 2.0, 80.0, 228.0, 20140529-HANULMIN-PC]
[0.0, 24.0, 0.0, 0.0, 0.0, 0.0, 24.0, 20140531-CHOIKYUMIN]

18. K-Means로 군집화 하기
K-Means 과정
- 클러스터링 개수 설정
3개

19. K-Means로 군집화 하기
Cluster 1
[1.0, 111.0, 242.0, 11.0, 65.0, 140.0, 570.0, 20140507-CHOIKYUMIN]
[9.0, 38.0, 127.0, 68.0, 14.0, 123.0, 378.0, 20140508-CHOIKYUMIN]
[3.0, 29.0, 293.0, 51.0, 20.0, 142.0, 538.0, 20140512-CHOIKYUMIN]
[2.0, 30.0, 263.0, 36.0, 143.0, 155.0, 628.0, 20140513-CHOIKYUMIN]
[4.0, 19.0, 168.0, 85.0, 196.0, 224.0, 696.0, 20140514-CHOIKYUMIN]
[2.0, 46.0, 262.0, 49.0, 25.0, 117.0, 502.0, 20140515-CHOIKYUMIN]
[1.0, 23.0, 241.0, 17.0, 11.0, 190.0, 484.0, 20140516-CHOIKYUMIN]
[2.0, 48.0, 226.0, 29.0, 31.0, 139.0, 475.0, 20140519-CHOIKYUMIN]
[1.0, 24.0, 162.0, 39.0, 11.0, 170.0, 408.0, 20140520-CHOIKYUMIN]
[7.0, 19.0, 41.0, 46.0, 2.0, 37.0, 151.0, 20140521-CHOIKYUMIN]
[8.0, 21.0, 250.0, 17.0, 5.0, 208.0, 509.0, 20140523-CHOIKYUMIN]
[5.0, 61.0, 302.0, 45.0, 22.0, 209.0, 644.0, 20140526-CHOIKYUMIN]
[2.0, 100.0, 117.0, 0.0, 23.0, 72.0, 315.0, 20140526-HANULMIN-PC]
[2.0, 17.0, 167.0, 50.0, 63.0, 220.0, 519.0, 20140527-CHOIKYUMIN]
[1.0, 22.0, 195.0, 61.0, 15.0, 293.0, 587.0, 20140528-CHOIKYUMIN]
[3.0, 23.0, 169.0, 48.0, 17.0, 170.0, 431.0, 20140529-CHOIKYUMIN]
[8.0, 27.0, 148.0, 31.0, 111.0, 265.0, 590.0, 20140530-CHOIKYUMIN]
[2.0, 0.0, 12.0, 17.0, 0.0, 22.0, 54.0, 20140530-HANULMIN-PC]
Cluster2
[0.0, 48.0, 0.0, 0.0, 0.0, 2.0, 51.0, 20140503-HANULMIN-PC]
[0.0, 150.0, 0.0, 0.0, 0.0, 22.0, 172.0, 20140504-HANULMIN-PC]
[1.0, 92.0, 6.0, 10.0, 5.0, 46.0, 160.0, 20140509-HANULMIN-PC]
[0.0, 19.0, 0.0, 13.0, 0.0, 18.0, 50.0, 20140514-HANULMIN-PC]
[0.0, 15.0, 0.0, 0.0, 0.0, 0.0, 15.0, 20140517-CHOIKYUMIN]
[0.0, 11.0, 0.0, 0.0, 0.0, 0.0, 11.0, 20140518-CHOIKYUMIN]
[6.0, 163.0, 8.0, 0.0, 8.0, 141.0, 327.0, 20140519-HANULMIN-PC]
[0.0, 135.0, 0.0, 0.0, 0.0, 47.0, 182.0, 20140521-HANULMIN-PC]
[1.0, 193.0, 0.0, 0.0, 0.0, 60.0, 254.0, 20140522-HANULMIN-PC]
[2.0, 62.0, 0.0, 14.0, 0.0, 59.0, 137.0, 20140523-HANULMIN-PC]
[0.0, 5.0, 0.0, 0.0, 0.0, 0.0, 5.0, 20140524-CHOIKYUMIN]
[0.0, 13.0, 0.0, 0.0, 0.0, 0.0, 13.0, 20140525-CHOIKYUMIN]
[5.0, 128.0, 0.0, 12.0, 2.0, 80.0, 228.0, 20140529-HANULMIN-PC]
[0.0, 24.0, 0.0, 0.0, 0.0, 0.0, 24.0, 20140531-CHOIKYUMIN]
Cluster3
[0.0, 0.0, 0.0, 0.0, 0.0, 20.0, 20.0, 20140508-HANULMIN-PC]
[4.0, 43.0, 20.0, 27.0, 54.0, 404.0, 552.0, 20140509-CHOIKYUMIN]
[0.0, 2.0, 0.0, 0.0, 33.0, 39.0, 74.0, 20140512-HANULMIN-PC]
[7.0, 67.0, 0.0, 0.0, 0.0, 102.0, 176.0, 20140513-HANULMIN-PC]
[3.0, 61.0, 0.0, 0.0, 88.0, 90.0, 242.0, 20140515-HANULMIN-PC]
[0.0, 15.0, 0.0, 0.0, 1.0, 88.0, 104.0, 20140518-HANULMIN-PC]
[0.0, 1.0, 0.0, 0.0, 1.0, 48.0, 49.0, 20140520-HANULMIN-PC]
[7.0, 61.0, 14.0, 0.0, 6.0, 166.0, 254.0, 20140525-HANULMIN-PC]
[12.0, 3.0, 0.0, 0.0, 0.0, 43.0, 58.0, 20140527-HANULMIN-PC]
[0.0, 0.0, 0.0, 0.0, 0.0, 14.0, 14.0, 20140531-HANULMIN-PC]

20. K-Means로 군집화 하기
4개

21. K-Means로 군집화 하기
Cluster 1
[1.0, 111.0, 242.0, 11.0, 65.0, 140.0, 570.0, 20140507-CHOIKYUMIN]
[3.0, 29.0, 293.0, 51.0, 20.0, 142.0, 538.0, 20140512-CHOIKYUMIN]
[2.0, 30.0, 263.0, 36.0, 143.0, 155.0, 628.0, 20140513-CHOIKYUMIN]
[2.0, 46.0, 262.0, 49.0, 25.0, 117.0, 502.0, 20140515-CHOIKYUMIN]
[1.0, 23.0, 241.0, 17.0, 11.0, 190.0, 484.0, 20140516-CHOIKYUMIN]
[2.0, 48.0, 226.0, 29.0, 31.0, 139.0, 475.0, 20140519-CHOIKYUMIN]
[8.0, 21.0, 250.0, 17.0, 5.0, 208.0, 509.0, 20140523-CHOIKYUMIN]
[5.0, 61.0, 302.0, 45.0, 22.0, 209.0, 644.0, 20140526-CHOIKYUMIN]
[2.0, 100.0, 117.0, 0.0, 23.0, 72.0, 315.0, 20140526-HANULMIN-PC]
Cluster2
[0.0, 0.0, 0.0, 0.0, 0.0, 20.0, 20.0, 20140508-HANULMIN-PC]
[4.0, 43.0, 20.0, 27.0, 54.0, 404.0, 552.0, 20140509-CHOIKYUMIN]
[0.0, 2.0, 0.0, 0.0, 33.0, 39.0, 74.0, 20140512-HANULMIN-PC]
[7.0, 67.0, 0.0, 0.0, 0.0, 102.0, 176.0, 20140513-HANULMIN-PC]
[3.0, 61.0, 0.0, 0.0, 88.0, 90.0, 242.0, 20140515-HANULMIN-PC]
[0.0, 15.0, 0.0, 0.0, 1.0, 88.0, 104.0, 20140518-HANULMIN-PC]
[0.0, 1.0, 0.0, 0.0, 1.0, 48.0, 49.0, 20140520-HANULMIN-PC]
[7.0, 61.0, 14.0, 0.0, 6.0, 166.0, 254.0, 20140525-HANULMIN-PC]
[12.0, 3.0, 0.0, 0.0, 0.0, 43.0, 58.0, 20140527-HANULMIN-PC]
[0.0, 0.0, 0.0, 0.0, 0.0, 14.0, 14.0, 20140531-HANULMIN-PC]
Cluster3
[9.0, 38.0, 127.0, 68.0, 14.0, 123.0, 378.0, 20140508-CHOIKYUMIN]
[4.0, 19.0, 168.0, 85.0, 196.0, 224.0, 696.0, 20140514-CHOIKYUMIN]
[1.0, 24.0, 162.0, 39.0, 11.0, 170.0, 408.0, 20140520-CHOIKYUMIN]
[7.0, 19.0, 41.0, 46.0, 2.0, 37.0, 151.0, 20140521-CHOIKYUMIN]
[2.0, 17.0, 167.0, 50.0, 63.0, 220.0, 519.0, 20140527-CHOIKYUMIN]
[1.0, 22.0, 195.0, 61.0, 15.0, 293.0, 587.0, 20140528-CHOIKYUMIN]
[3.0, 23.0, 169.0, 48.0, 17.0, 170.0, 431.0, 20140529-CHOIKYUMIN]
[8.0, 27.0, 148.0, 31.0, 111.0, 265.0, 590.0, 20140530-CHOIKYUMIN]
[2.0, 0.0, 12.0, 17.0, 0.0, 22.0, 54.0, 20140530-HANULMIN-PC]
Cluster4
[0.0, 48.0, 0.0, 0.0, 0.0, 2.0, 51.0, 20140503-HANULMIN-PC]
[0.0, 150.0, 0.0, 0.0, 0.0, 22.0, 172.0, 20140504-HANULMIN-PC]
[1.0, 92.0, 6.0, 10.0, 5.0, 46.0, 160.0, 20140509-HANULMIN-PC]
[0.0, 19.0, 0.0, 13.0, 0.0, 18.0, 50.0, 20140514-HANULMIN-PC]
[0.0, 15.0, 0.0, 0.0, 0.0, 0.0, 15.0, 20140517-CHOIKYUMIN]
[0.0, 11.0, 0.0, 0.0, 0.0, 0.0, 11.0, 20140518-CHOIKYUMIN]
[6.0, 163.0, 8.0, 0.0, 8.0, 141.0, 327.0, 20140519-HANULMIN-PC]
[0.0, 135.0, 0.0, 0.0, 0.0, 47.0, 182.0, 20140521-HANULMIN-PC]
[1.0, 193.0, 0.0, 0.0, 0.0, 60.0, 254.0, 20140522-HANULMIN-PC]
[2.0, 62.0, 0.0, 14.0, 0.0, 59.0, 137.0, 20140523-HANULMIN-PC]
[0.0, 5.0, 0.0, 0.0, 0.0, 0.0, 5.0, 20140524-CHOIKYUMIN]
[0.0, 13.0, 0.0, 0.0, 0.0, 0.0, 13.0, 20140525-CHOIKYUMIN]
[5.0, 128.0, 0.0, 12.0, 2.0, 80.0, 228.0, 20140529-HANULMIN-PC]
[0.0, 24.0, 0.0, 0.0, 0.0, 0.0, 24.0, 20140531-CHOIKYUMIN]

22. K-Means로 군집화 하기
5개

23. K-Means로 군집화 하기
Cluster 1
[1.0, 92.0, 6.0, 10.0, 5.0, 46.0, 160.0, 20140509-HANULMIN-PC]
[7.0, 67.0, 0.0, 0.0, 0.0, 102.0, 176.0, 20140513-HANULMIN-PC]
[0.0, 19.0, 0.0, 13.0, 0.0, 18.0, 50.0, 20140514-HANULMIN-PC]
[3.0, 61.0, 0.0, 0.0, 88.0, 90.0, 242.0, 20140515-HANULMIN-PC]
[6.0, 163.0, 8.0, 0.0, 8.0, 141.0, 327.0, 20140519-HANULMIN-PC]
[2.0, 62.0, 0.0, 14.0, 0.0, 59.0, 137.0, 20140523-HANULMIN-PC]
[5.0, 128.0, 0.0, 12.0, 2.0, 80.0, 228.0, 20140529-HANULMIN-PC]
Cluster2
[1.0, 111.0, 242.0, 11.0, 65.0, 140.0, 570.0, 20140507-CHOIKYUMIN]
[3.0, 29.0, 293.0, 51.0, 20.0, 142.0, 538.0, 20140512-CHOIKYUMIN]
[2.0, 46.0, 262.0, 49.0, 25.0, 117.0, 502.0, 20140515-CHOIKYUMIN]
[1.0, 23.0, 241.0, 17.0, 11.0, 190.0, 484.0, 20140516-CHOIKYUMIN]
[2.0, 48.0, 226.0, 29.0, 31.0, 139.0, 475.0, 20140519-CHOIKYUMIN]
[8.0, 21.0, 250.0, 17.0, 5.0, 208.0, 509.0, 20140523-CHOIKYUMIN]
[5.0, 61.0, 302.0, 45.0, 22.0, 209.0, 644.0, 20140526-CHOIKYUMIN]
[2.0, 100.0, 117.0, 0.0, 23.0, 72.0, 315.0, 20140526-HANULMIN-PC]
Cluster3
[0.0, 0.0, 0.0, 0.0, 0.0, 20.0, 20.0, 20140508-HANULMIN-PC]
[4.0, 43.0, 20.0, 27.0, 54.0, 404.0, 552.0, 20140509-CHOIKYUMIN]
[0.0, 2.0, 0.0, 0.0, 33.0, 39.0, 74.0, 20140512-HANULMIN-PC]
[0.0, 15.0, 0.0, 0.0, 1.0, 88.0, 104.0, 20140518-HANULMIN-PC]
[0.0, 1.0, 0.0, 0.0, 1.0, 48.0, 49.0, 20140520-HANULMIN-PC]
[7.0, 61.0, 14.0, 0.0, 6.0, 166.0, 254.0, 20140525-HANULMIN-PC]
[12.0, 3.0, 0.0, 0.0, 0.0, 43.0, 58.0, 20140527-HANULMIN-PC]
[0.0, 0.0, 0.0, 0.0, 0.0, 14.0, 14.0, 20140531-HANULMIN-PC]
Cluster4
[0.0, 48.0, 0.0, 0.0, 0.0, 2.0, 51.0, 20140503-HANULMIN-PC]
[0.0, 150.0, 0.0, 0.0, 0.0, 22.0, 172.0, 20140504-HANULMIN-PC]
[0.0, 15.0, 0.0, 0.0, 0.0, 0.0, 15.0, 20140517-CHOIKYUMIN]
[0.0, 11.0, 0.0, 0.0, 0.0, 0.0, 11.0, 20140518-CHOIKYUMIN]
[0.0, 135.0, 0.0, 0.0, 0.0, 47.0, 182.0, 20140521-HANULMIN-PC]
[1.0, 193.0, 0.0, 0.0, 0.0, 60.0, 254.0, 20140522-HANULMIN-PC]
[0.0, 5.0, 0.0, 0.0, 0.0, 0.0, 5.0, 20140524-CHOIKYUMIN]
[0.0, 13.0, 0.0, 0.0, 0.0, 0.0, 13.0, 20140525-CHOIKYUMIN]
[0.0, 24.0, 0.0, 0.0, 0.0, 0.0, 24.0, 20140531-CHOIKYUMIN]
Cluster5
[9.0, 38.0, 127.0, 68.0, 14.0, 123.0, 378.0, 20140508-CHOIKYUMIN]
[2.0, 30.0, 263.0, 36.0, 143.0, 155.0, 628.0, 20140513-CHOIKYUMIN]
[4.0, 19.0, 168.0, 85.0, 196.0, 224.0, 696.0, 20140514-CHOIKYUMIN]
[1.0, 24.0, 162.0, 39.0, 11.0, 170.0, 408.0, 20140520-CHOIKYUMIN]
[7.0, 19.0, 41.0, 46.0, 2.0, 37.0, 151.0, 20140521-CHOIKYUMIN]
[2.0, 17.0, 167.0, 50.0, 63.0, 220.0, 519.0, 20140527-CHOIKYUMIN]
[1.0, 22.0, 195.0, 61.0, 15.0, 293.0, 587.0, 20140528-CHOIKYUMIN]
[3.0, 23.0, 169.0, 48.0, 17.0, 170.0, 431.0, 20140529-CHOIKYUMIN]
[8.0, 27.0, 148.0, 31.0, 111.0, 265.0, 590.0, 20140530-CHOIKYUMIN]
[2.0, 0.0, 12.0, 17.0, 0.0, 22.0, 54.0, 20140530-HANULMIN-PC]

24. 이제 이것으로 무엇을 하나?
Office-PC 데이터 내에서 군집화 하기

25. 이제 이것으로 무엇을 하나?
Office-PC 데이터 내에서 군집화 하기
생산성이좋은날vs 나쁜날??
회의가많은날vs 없는날??
잡일을많이하는는vs 개발에집중하는날??

26. K-Means로 군집화 하기
4개

27. 이제 이것으로 무엇을 하나?
Cluster 1
[9.0, 38.0, 127.0, 68.0, 14.0, 123.0, 378.0, 20140508-OFFICE]
[1.0, 23.0, 241.0, 17.0, 11.0, 190.0, 484.0, 20140516-OFFICE]
[1.0, 24.0, 162.0, 39.0, 11.0, 170.0, 408.0, 20140520-OFFICE]
[7.0, 19.0, 41.0, 46.0, 2.0, 37.0, 151.0, 20140521-OFFICE]
[8.0, 21.0, 250.0, 17.0, 5.0, 208.0, 509.0, 20140523-OFFICE]
[2.0, 17.0, 167.0, 50.0, 63.0, 220.0, 519.0, 20140527-OFFICE]
[1.0, 22.0, 195.0, 61.0, 15.0, 293.0, 587.0, 20140528-OFFICE]
[3.0, 23.0, 169.0, 48.0, 17.0, 170.0, 431.0, 20140529-OFFICE]
Cluster2
[1.0, 111.0, 242.0, 11.0, 65.0, 140.0, 570.0, 20140507-OFFICE]
[3.0, 29.0, 293.0, 51.0, 20.0, 142.0, 538.0, 20140512-OFFICE]
[2.0, 30.0, 263.0, 36.0, 143.0, 155.0, 628.0, 20140513-OFFICE]
[2.0, 46.0, 262.0, 49.0, 25.0, 117.0, 502.0, 20140515-OFFICE]
[2.0, 48.0, 226.0, 29.0, 31.0, 139.0, 475.0, 20140519-OFFICE]
[5.0, 61.0, 302.0, 45.0, 22.0, 209.0, 644.0, 20140526-OFFICE]
Cluster3
[0.0, 15.0, 0.0, 0.0, 0.0, 0.0, 15.0, 20140517-OFFICE]
[0.0, 11.0, 0.0, 0.0, 0.0, 0.0, 11.0, 20140518-OFFICE]
[0.0, 5.0, 0.0, 0.0, 0.0, 0.0, 5.0, 20140524-OFFICE]
[0.0, 13.0, 0.0, 0.0, 0.0, 0.0, 13.0, 20140525-OFFICE]
[0.0, 24.0, 0.0, 0.0, 0.0, 0.0, 24.0, 20140531-OFFICE]
Cluster4
[4.0, 43.0, 20.0, 27.0, 54.0, 404.0, 552.0, 20140509-OFFICE]
[4.0, 19.0, 168.0, 85.0, 196.0, 224.0, 696.0, 20140514-OFFICE]
[8.0, 27.0, 148.0, 31.0, 111.0, 265.0, 590.0, 20140530-OFFICE]
뭐 끼리 군집화 된 거지 ??

28. 이제 이것으로 무엇을 하나?
Cluster 1 – 05/23
[9.0, 38.0, 127.0, 68.0, 14.0, 123.0, 378.0, 20140508-OFFICE]
[1.0, 23.0, 241.0, 17.0, 11.0, 190.0, 484.0, 20140516-OFFICE]
[1.0, 24.0, 162.0, 39.0, 11.0, 170.0, 408.0, 20140520-OFFICE]
[7.0, 19.0, 41.0, 46.0, 2.0, 37.0, 151.0, 20140521-OFFICE]
[8.0, 21.0, 250.0, 17.0, 5.0, 208.0, 509.0, 20140523-OFFICE]
[2.0, 17.0, 167.0, 50.0, 63.0, 220.0, 519.0, 20140527-OFFICE]
[1.0, 22.0, 195.0, 61.0, 15.0, 293.0, 587.0, 20140528-OFFICE]
[3.0, 23.0, 169.0, 48.0, 17.0, 170.0, 431.0, 20140529-OFFICE]
Cluster2 – 05/15
[1.0, 111.0, 242.0, 11.0, 65.0, 140.0, 570.0, 20140507-OFFICE]
[3.0, 29.0, 293.0, 51.0, 20.0, 142.0, 538.0, 20140512-OFFICE]
[2.0, 30.0, 263.0, 36.0, 143.0, 155.0, 628.0, 20140513-OFFICE]
[2.0, 46.0, 262.0, 49.0, 25.0, 117.0, 502.0, 20140515-OFFICE]
[2.0, 48.0, 226.0, 29.0, 31.0, 139.0, 475.0, 20140519-OFFICE]
[5.0, 61.0, 302.0, 45.0, 22.0, 209.0, 644.0, 20140526-OFFICE]
Cluster3 – 05/24
[0.0, 15.0, 0.0, 0.0, 0.0, 0.0, 15.0, 20140517-OFFICE]
[0.0, 11.0, 0.0, 0.0, 0.0, 0.0, 11.0, 20140518-OFFICE]
[0.0, 5.0, 0.0, 0.0, 0.0, 0.0, 5.0, 20140524-OFFICE]
[0.0, 13.0, 0.0, 0.0, 0.0, 0.0, 13.0, 20140525-OFFICE]
[0.0, 24.0, 0.0, 0.0, 0.0, 0.0, 24.0, 20140531-OFFICE]
Cluster4 – 05/14
[4.0, 43.0, 20.0, 27.0, 54.0, 404.0, 552.0, 20140509-OFFICE]
[4.0, 19.0, 168.0, 85.0, 196.0, 224.0, 696.0, 20140514-OFFICE]
[8.0, 27.0, 148.0, 31.0, 111.0, 265.0, 590.0, 20140530-OFFICE]
중간 값의 세부 데이터를 보자

29. 이제 이것으로 무엇을 하나?
Cluster 1 – 05/23
[9.0, 38.0, 127.0, 68.0, 14.0, 123.0, 378.0, 20140508-OFFICE]
[1.0, 23.0, 241.0, 17.0, 11.0, 190.0, 484.0, 20140516-OFFICE]
[1.0, 24.0, 162.0, 39.0, 11.0, 170.0, 408.0, 20140520-OFFICE]
[7.0, 19.0, 41.0, 46.0, 2.0, 37.0, 151.0, 20140521-OFFICE]
[8.0, 21.0, 250.0, 17.0, 5.0, 208.0, 509.0, 20140523-OFFICE]
[2.0, 17.0, 167.0, 50.0, 63.0, 220.0, 519.0, 20140527-OFFICE]
[1.0, 22.0, 195.0, 61.0, 15.0, 293.0, 587.0, 20140528-OFFICE]
[3.0, 23.0, 169.0, 48.0, 17.0, 170.0, 431.0, 20140529-OFFICE]
Cluster2 – 05/15
[1.0, 111.0, 242.0, 11.0, 65.0, 140.0, 570.0, 20140507-OFFICE]
[3.0, 29.0, 293.0, 51.0, 20.0, 142.0, 538.0, 20140512-OFFICE]
[2.0, 30.0, 263.0, 36.0, 143.0, 155.0, 628.0, 20140513-OFFICE]
[2.0, 46.0, 262.0, 49.0, 25.0, 117.0, 502.0, 20140515-OFFICE]
[2.0, 48.0, 226.0, 29.0, 31.0, 139.0, 475.0, 20140519-OFFICE]
[5.0, 61.0, 302.0, 45.0, 22.0, 209.0, 644.0, 20140526-OFFICE]
Cluster3 – 05/24
[0.0, 15.0, 0.0, 0.0, 0.0, 0.0, 15.0, 20140517-OFFICE]
[0.0, 11.0, 0.0, 0.0, 0.0, 0.0, 11.0, 20140518-OFFICE]
[0.0, 5.0, 0.0, 0.0, 0.0, 0.0, 5.0, 20140524-OFFICE]
[0.0, 13.0, 0.0, 0.0, 0.0, 0.0, 13.0, 20140525-OFFICE]
[0.0, 24.0, 0.0, 0.0, 0.0, 0.0, 24.0, 20140531-OFFICE]
Cluster4 – 05/14
[4.0, 43.0, 20.0, 27.0, 54.0, 404.0, 552.0, 20140509-OFFICE]
[4.0, 19.0, 168.0, 85.0, 196.0, 224.0, 696.0, 20140514-OFFICE]
[8.0, 27.0, 148.0, 31.0, 111.0, 265.0, 590.0, 20140530-OFFICE]

30. 이제 이것으로 무엇을 하나?
Cluster 1 – 05/23
[9.0, 38.0, 127.0, 68.0, 14.0, 123.0, 378.0, 20140508-OFFICE]
[1.0, 23.0, 241.0, 17.0, 11.0, 190.0, 484.0, 20140516-OFFICE]
[1.0, 24.0, 162.0, 39.0, 11.0, 170.0, 408.0, 20140520-OFFICE]
[7.0, 19.0, 41.0, 46.0, 2.0, 37.0, 151.0, 20140521-OFFICE]
[8.0, 21.0, 250.0, 17.0, 5.0, 208.0, 509.0, 20140523-OFFICE]
[2.0, 17.0, 167.0, 50.0, 63.0, 220.0, 519.0, 20140527-OFFICE]
[1.0, 22.0, 195.0, 61.0, 15.0, 293.0, 587.0, 20140528-OFFICE]
[3.0, 23.0, 169.0, 48.0, 17.0, 170.0, 431.0, 20140529-OFFICE]
Cluster2 – 05/15
[1.0, 111.0, 242.0, 11.0, 65.0, 140.0, 570.0, 20140507-OFFICE]
[3.0, 29.0, 293.0, 51.0, 20.0, 142.0, 538.0, 20140512-OFFICE]
[2.0, 30.0, 263.0, 36.0, 143.0, 155.0, 628.0, 20140513-OFFICE]
[2.0, 46.0, 262.0, 49.0, 25.0, 117.0, 502.0, 20140515-OFFICE]
[2.0, 48.0, 226.0, 29.0, 31.0, 139.0, 475.0, 20140519-OFFICE]
[5.0, 61.0, 302.0, 45.0, 22.0, 209.0, 644.0, 20140526-OFFICE]
Cluster3 – 05/24
[0.0, 15.0, 0.0, 0.0, 0.0, 0.0, 15.0, 20140517-OFFICE]
[0.0, 11.0, 0.0, 0.0, 0.0, 0.0, 11.0, 20140518-OFFICE]
[0.0, 5.0, 0.0, 0.0, 0.0, 0.0, 5.0, 20140524-OFFICE]
[0.0, 13.0, 0.0, 0.0, 0.0, 0.0, 13.0, 20140525-OFFICE]
[0.0, 24.0, 0.0, 0.0, 0.0, 0.0, 24.0, 20140531-OFFICE]
Cluster4 – 05/14
[4.0, 43.0, 20.0, 27.0, 54.0, 404.0, 552.0, 20140509-OFFICE]
[4.0, 19.0, 168.0, 85.0, 196.0, 224.0, 696.0, 20140514-OFFICE]
[8.0, 27.0, 148.0, 31.0, 111.0, 265.0, 590.0, 20140530-OFFICE]
쉬엄쉬엄한날 집중력있게개발한다.
집중력있게잡일한다.일안한날

31. 이제 이것으로 무엇을 하나?
군집화를 이용한
Personal Analytics 한 것 같은데!!

32. 감사합니다.
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