추놀 5회 무엇이든 분류해 보기

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

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추놀 5회 무엇이든 분류해 보기

  1. 1. 추천아 놀자 5회 무엇이든 군집화하기 ( K-means 좀더) 곧 시작함
  2. 2. RescueTime 에 대하여 자신의 PC의 App, 웹사이트 등 사용시간을 기록하여 카테고리를 분류하여 생산성을 측정해 주는 도구
  3. 3. RescueTime 에 대하여 갑자기 왜?
  4. 4. 오늘 분류할 데이터 셋이 내 PC의 APP 사용 시간을 기록한 데이터 입니다. RescueTime 에 대하여
  5. 5. 좀더 알아 봅시다. http://rescuetime.com 무료 버젼 RescueTime 에 대하여
  6. 6. 우리가 사용한 데이터 셋 PC App별 사용 시간 측정( 초단위 )
  7. 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. 8. 우리가 사용할 데이터 셋 2014/05/01~ 05/31 기간의 내 Office-PC와 Home-PC의 PC App의 사용 시간
  9. 9. 우리가 사용한 데이터 셋 일자별로 카테고리 분류별로 사용 시간 (초) 측정 레알!!! 실제 데이터
  10. 10. 이제 이것으로 무엇을 하나? Office-PC 데이터끼리 Home-PC 데이터끼리 데이터군집화를해보자
  11. 11. 이제 이것으로 무엇을 하나? 어떻게?? K-Means 군집화 알고리즘!!
  12. 12. 주어진데이터를K개의군집으로나누는알고리즘이다. ①나눌군집개수K를결정 ②임의의군집중심으로가까운점들끼리묶음 ③각각의군집에대하여평균을새로구함 ④새로운평균의중심값으로가장근접한점들끼리묶음 ⑤3번,4번단계를반복적으로수행하여변경이없을때까지수행 ① ② ③ ④ ⑤ K-Means 군집화
  13. 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. 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. 15. K-Means로 군집화 하기 K-Means 과정 - 클러스터링 개수 설정 2개
  16. 16. K-Means로 군집화 하기
  17. 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. 18. K-Means로 군집화 하기 K-Means 과정 - 클러스터링 개수 설정 3개
  19. 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. 20. K-Means로 군집화 하기 4개
  21. 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. 22. K-Means로 군집화 하기 5개
  23. 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. 24. 이제 이것으로 무엇을 하나? Office-PC 데이터 내에서 군집화 하기
  25. 25. 이제 이것으로 무엇을 하나? Office-PC 데이터 내에서 군집화 하기 생산성이좋은날vs 나쁜날?? 회의가많은날vs 없는날?? 잡일을많이하는는vs 개발에집중하는날??
  26. 26. K-Means로 군집화 하기 4개
  27. 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. 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. 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. 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. 31. 이제 이것으로 무엇을 하나? 군집화를 이용한 Personal Analytics 한 것 같은데!!
  32. 32. 감사합니다. 방송국 : Afreecatv.com/goodvc 블로그 : goodvc78.postach.io

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