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Hierarchical clustering of unequal-length time series with area-based shape distance

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Abstract

Time-series clustering algorithms have been used in a variety of areas to extract valuable information from complex and massive data sets. However, these algorithms suffer from two shortcomings. On the one hand, most of them are designed for the equal-length time series, while clustering of unequal-length time series is often encountered in real-world problems. On the other hand, commonly used distance measures of time series cannot fully reveal trend differences. To overcome these two shortcomings, this paper focuses on the trend of time series and employs the area-based shape distance to measure their similarity. In addition, we present a new hierarchical clustering for unequal-length time series based on area-based shape distance measure. A series of experiments illustrates the performance of the proposed clustering algorithm.

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Acknowledgements

This work was funded by National Natural Science Foundation of China (Nos. 11701338, 11571001), Natural Science Foundation of Shandong Province (No. ZR2016AP12), and a Project of Shandong Province Higher Educational Science and Technology Program (No. J17KB124).

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Authors and Affiliations

  1. School of Information Engineering, Shandong Youth University of Political Science, Jinan, 250103, China

    Xiao Wang

  2. School of Mathematical Sciences, Laboratory of Mathematics and Complex Systems, Ministry of Education, Beijing Normal University, Beijing, 100875, China

    Fusheng Yu & Jiayin Wang

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6R 2V4, Canada

    Witold Pedrycz

Authors
  1. Xiao Wang
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  2. Fusheng Yu
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  3. Witold Pedrycz
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  4. Jiayin Wang
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Correspondence to Fusheng Yu.

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Wang, X., Yu, F., Pedrycz, W. et al. Hierarchical clustering of unequal-length time series with area-based shape distance. Soft Comput 23, 6331–6343 (2019). https://doi.org/10.1007/s00500-018-3287-6

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