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International Conference on Advanced Data Mining and Applications

ADMA 2018: Advanced Data Mining and Applications pp 31–41Cite as

HierArchical-Grid CluStering Based on DaTA Field in Time-Series and the Influence of the First-Order Partial Derivative Potential Value for the ARIMA-Model

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11323))

Abstract

Extend the function of static-time dataframe clustering algorithm (HASTA: HierArchical-grid cluStering based on daTA field) to be able to cluster the time-series dataframe. The algorithm purposed to use a set of “first-partial derivative potential value” given from HASTA in the multiple dataframes as the input to the autoregressive integrated moving average (ARIMA) under preliminary parameters. The ARIMA model could perform the pre-labeling task for the cluster(s) in the connected dataframe on the same time-series data. Calculating the structural similarity as a distance measure between timeframe, ARIMA would mark the high potential grid(s) as the cluster tracker. As the result, the ARIMA model could interpreting and reasoning cluster movement phenomena in the systematic approach. This integration is the attempt to show the influent power of data field in the term of knowledge representation.

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Acknowledgment

This work was supported by the National Key Research and Development Program of China (No.2016YFB0502600), the National Natural Science Fund of China (61472039), Beijing Institute of Technology International Cooperation Project (GZ2016085103), and Open Fund of Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoformation (2017NGCMZD03).

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Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

    Krid Jinklub & Jing Geng

Authors
  1. Krid Jinklub
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  2. Jing Geng
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Corresponding author

Correspondence to Jing Geng .

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Editors and Affiliations

  1. University of Connecticut, Storrs, CT, USA

    Guojun Gan

  2. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Bohan Li

  3. The University of Queensland, Brisbane, QLD, Australia

    Xue Li

  4. Beijing Institute of Technology, Beijing, China

    Shuliang Wang

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Jinklub, K., Geng, J. (2018). HierArchical-Grid CluStering Based on DaTA Field in Time-Series and the Influence of the First-Order Partial Derivative Potential Value for the ARIMA-Model. In: Gan, G., Li, B., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2018. Lecture Notes in Computer Science(), vol 11323. Springer, Cham. https://doi.org/10.1007/978-3-030-05090-0_3

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  • DOI: https://doi.org/10.1007/978-3-030-05090-0_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05089-4

  • Online ISBN: 978-3-030-05090-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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