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
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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.
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.
Wang ZF, Pan Q, Lang L, et al. Dynamic track clustering algorithm based on subtractive clustering. Syst Simul J. 2009;21(16):5240–3.
Chen H, Zhang BY, Chen Y. Research on multi-hypothesis tracking with adaptive depth adaptation. Syst Eng Electron Technol. 2016;38(9):2000–7.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yan, G., Yaobin, L., Lijiang, N., Jing, W. (2020). Improved K-Means Clustering for Target Activity Regular Pattern Extraction with Big Data Mining. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_123
Download citation
DOI: https://doi.org/10.1007/978-981-13-6504-1_123
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6503-4
Online ISBN: 978-981-13-6504-1
eBook Packages: EngineeringEngineering (R0)