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Feature Extraction of Network Temporal and Spatial Distribution Based on Data Stream Clustering

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Multimedia Technology and Enhanced Learning (ICMTEL 2021)

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Abstract

Spatio-temporal data contains a lot of knowledge information, including patterns and characteristics, the relationship between data and data and their characteristics, etc. How to extract these characteristics from these spatio-temporal data has become the focus of research. To this end, a method of network spatiotemporal distribution feature extraction based on data stream clustering is studied. This method first analyzes the relevant theories of the clustering algorithm, and then uses the DBSCAN algorithm in the clustering algorithm to extract the characteristics of network temporal and spatial distribution. The results show that: compared with the traditional extraction method, the extraction quality of this method is higher, reaching the goal of this paper.

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Authors and Affiliations

  1. School of Intelligence Technology, Geely University, Chengdu, 641402, China

    Hu Rong

  2. Chengdu College of University of Electronic Science and Technology of China, Chengdu, 641402, China

    Luo Dan

Authors
  1. Hu Rong
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  2. Luo Dan
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Editor information

Editors and Affiliations

  1. Hunan Normal University, Changsha, China

    Weina Fu

  2. University of Jinan, Jinan, China

    Yuan Xu

  3. University of Leicester, Leicester, UK

    Shui-Hua Wang

  4. University of Leicester, Leicester, UK

    Yudong Zhang

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Rong, H., Dan, L. (2021). Feature Extraction of Network Temporal and Spatial Distribution Based on Data Stream Clustering. In: Fu, W., Xu, Y., Wang, SH., Zhang, Y. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 387. Springer, Cham. https://doi.org/10.1007/978-3-030-82562-1_53

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  • DOI: https://doi.org/10.1007/978-3-030-82562-1_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-82561-4

  • Online ISBN: 978-3-030-82562-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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