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Dynamic time alignment kernel-based fuzzy clustering of non-equal length vector time series

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Abstract

Time series clustering is an effective vehicle to explore and visualize the structure of a suite of time series. In this study, we generalize the kernel-based fuzzy c-means clustering algorithm by involving the dynamic time alignment kernel (DTAK) to cluster vector time series. In this method, the nonlinear time alignment embedded in DTAK makes the kernel-based fuzzy c-means available for sequences with variable lengths. However, it is noted that DTAK is not a strictly positive definite kernel, especially when the sample size is large. To overcome this, some strategies are presented to make the proposed algorithm available for large data sets. In addition, it is a challenge task to calculate the average sequence for a series of time series with different lengths. In kernel-based fuzzy c-means algorithm, it is not necessary to calculate the average sequence, which will increase the effectiveness of clustering techniques for time series. In the experiments, the kernel-based fuzzy c-means with DTAK is evaluated by both the data sets from the UCI KDD Archive and real-world data sets. Experimental results delivered by the proposed method demonstrate its effectiveness and robustness.

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Acknowledgements

This work is supported by the Natural Science Foundation of China under Grant 71831002, 61773352, Program for Innovative Research Team in University of Ministry of Education of China IRT_17R13, the Postdoctoral Science Foundation of China under Grant 2019M651100, the Natural Science Foundation of Liaoning Province 2019-BS-029, the Scocial Science Foundation of Liaoning Province L18DGL010, and the Fundamental Research Funds for the Central Universities 3132019501, 3132019502.

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Authors and Affiliations

  1. School of Maritime Economics and Management, Dalian Maritime University, Dalian, 116026, China

    Hongyue Guo

  2. Collaborative Innovation Center for Transport Studies, Dalian Maritime University, Dalian, 116026, China

    Hongyue Guo

  3. College of Science, Dalian Maritime University, Dalian, 116026, China

    Lidong Wang

  4. School of Control Science and Engineering, Dalian University of Technology, Dalian, 116024, China

    Xiaodong Liu

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  1. Hongyue Guo
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  2. Lidong Wang
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  3. Xiaodong Liu
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Correspondence to Hongyue Guo.

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Guo, H., Wang, L. & Liu, X. Dynamic time alignment kernel-based fuzzy clustering of non-equal length vector time series. Int. J. Mach. Learn. & Cyber. 10, 3167–3179 (2019). https://doi.org/10.1007/s13042-019-01007-3

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