Advertisement

Improved K-Means Clustering for Target Activity Regular Pattern Extraction with Big Data Mining

  • Guo YanEmail author
  • Lu Yaobin
  • Ning Lijiang
  • Wang Jing
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)

Abstract

The traditional target activity regular pattern extraction methods replay previous target tracks, activities of the specified target are manually analyzed by checking all the tracks on map. This paper adopts big data mining technology to solve the problem of automatically extracting target classic tracks and converts the original pure manual map analysis into system automatic track extraction. This method greatly reduces the operation intervention of classic track extraction, which can reduce the 3–4 manual days to 3–4 h.

Keywords

Big data mining K-means clustering Target activity regular pattern 

References

  1. 1.
    Liang JY, Qian YH, Li DY, et al. Theory and method of granular computing for big data mining. China Sci Inf Sci. 2015;45(11):1355–69.Google Scholar
  2. 2.
    Xu T, Li YX, Lv ZP. Track clustering based on distance from the track point metod. Syst Eng Electron Technol. 2015;37(9):2198–204.Google Scholar
  3. 3.
    Wang ZF, Pan Q, Lang L, et al. Dynamic track clustering algorithm based on subtractive clustering. Syst Simul J. 2009;21(16):5240–3.Google Scholar
  4. 4.
    Chen H, Zhang BY, Chen Y. Research on multi-hypothesis tracking with adaptive depth adaptation. Syst Eng Electron Technol. 2016;38(9):2000–7.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Nanjing Institute of Electronic TechnologyNanjingChina
  2. 2.Troop, PLABeijingChina

Personalised recommendations