• DocumentCode
    2341927
  • Title

    Study on Regional Division Based on Self-Adaptive FCM Clustering

  • Author

    Li, Shengdong ; Lv, Xueqiang ; Ling, Feng ; Shi, Shuicai

  • Author_Institution
    Chinese Inf. Process. Res. Center, Beijing Inf. Sci. & Technol. Univ., Beijing, China
  • fYear
    2010
  • fDate
    23-25 April 2010
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Through researching and analyzing self-adaptive strategy and fuzzy C-means (FCM) clustering algorithm, we put them together to form a self-adaptive FCM clustering algorithm. It is a good solution to the problem of local optimum as well as sensitivity to the initial value for the traditional FCM clustering algorithm. Finally, the new algorithm has been used in the regional division of police patrols in a city. In the division of the region, it has been proved by experiments that the sum of distance between a police vehicle and each possible accident scene can achieve the minimum value, which shows a significant effect of police patrols. And through the improved dijkstra algorithm to calculate shortest path length between a police vehicle and an accident scene, it proves that a police vehicle in the division of the region arrives at an accident scene within three minutes after accepting the warnings, whose proportion is 90.2%.
  • Keywords
    fuzzy set theory; pattern clustering; Dijkstra algorithm; accident scene; fuzzy C-means clustering; police vehicle; regional division; self-adaptive FCM clustering; shortest path length; Algorithm design and analysis; Cities and towns; Clustering algorithms; Information processing; Information science; Information technology; Layout; Machine learning algorithms; Road accidents; Vehicles;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biomedical Engineering and Computer Science (ICBECS), 2010 International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-5315-3
  • Type

    conf

  • DOI
    10.1109/ICBECS.2010.5462522
  • Filename
    5462522