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International Conference in Communications, Signal Processing, and Systems

CSPS 2018: Communications, Signal Processing, and Systems pp 1037–1043Cite as

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

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Part of the book series: Lecture Notes in Electrical Engineering ((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.

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References

  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.

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  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.

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  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.

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  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.

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Author information

Authors and Affiliations

  1. Nanjing Institute of Electronic Technology, Guorui Road 8#, Nanjing, China

    Guo Yan, Lu Yaobin & Ning Lijiang

  2. Troop, PLA, Beijing, 66132, China

    Wang Jing

Authors
  1. Guo Yan
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  2. Lu Yaobin
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  3. Ning Lijiang
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  4. Wang Jing
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Corresponding author

Correspondence to Guo Yan .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. School of Information and Communication Engineering, Dalian University of Technology, Dalian, China

    Xin Liu

  3. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Jiasong Mu

  6. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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© 2020 Springer Nature Singapore Pte Ltd.

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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

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  • 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)

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