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Trend analysis using agglomerative hierarchical clustering approach for time series big data

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Abstract

Road traffic accidents are a ‘global tragedy’ that generates unpredictable chunks of data having heterogeneity. To avoid this heterogeneous tragedy, we need to fraternize and categorize the datasets. This can be done with the help of clustering and association rule mining techniques. As the trend of accidents is increasing throughout the year, agglomerative hierarchical clustering approach is proposed for time series big data for trend analysis. This clustering approach segments the time sequence data into different clusters after normalizing the discrete time sequence data. Agglomerative hierarchical clustering takes the objects with similar properties and groups them together to form the group of clusters. The paradigmatic time sequence (PTS) data for each cluster with the help of dynamic time warping are identified that calculate the closest time sequence. The PTS analyzes various zone details and forms a cluster to report the data. This approach is more useful and optimal than the traditional statistical techniques.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1F1A1076976).

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Authors and Affiliations

  1. School of Computing, Scope, VIT University, Chennai Campus, Tamilnadu, India

    Subbulakshmi Pasupathi

  2. Department of IT, National Engineering College, Kovilpatti, Tamil Nadu, India

    Vimal Shanmuganathan

  3. Department of CSE, Ramco Institute of Technology, Rajapalayam, Tamilnadu, India

    Kaliappan Madasamy

  4. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

    Harold Robinson Yesudhas

  5. School of Computer Science and Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, South Korea

    Mucheol Kim

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  1. Subbulakshmi Pasupathi
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  2. Vimal Shanmuganathan
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  3. Kaliappan Madasamy
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  4. Harold Robinson Yesudhas
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  5. Mucheol Kim
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Correspondence to Mucheol Kim.

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Pasupathi, S., Shanmuganathan, V., Madasamy, K. et al. Trend analysis using agglomerative hierarchical clustering approach for time series big data. J Supercomput 77, 6505–6524 (2021). https://doi.org/10.1007/s11227-020-03580-9

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  • DOI: https://doi.org/10.1007/s11227-020-03580-9

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