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The Journal of Supercomputing

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02-01-2021

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

Authors: Subbulakshmi Pasupathi, Vimal Shanmuganathan, Kaliappan Madasamy, Harold Robinson Yesudhas, Mucheol Kim

Published in: The Journal of Supercomputing | Issue 7/2021

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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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Title: Trend analysis using agglomerative hierarchical clustering approach for time series big data
Authors: Subbulakshmi Pasupathi
Vimal Shanmuganathan
Kaliappan Madasamy
Harold Robinson Yesudhas
Mucheol Kim
Publication date: 02-01-2021
Publisher: Springer US
Published in: The Journal of Supercomputing / Issue 7/2021
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-020-03580-9

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