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SWS: an unsupervised trajectory segmentation algorithm based on change detection with interpolation kernels

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Abstract

Trajectory mining aims to provide fundamental insights into decision-making tasks related to moving objects. A fundamental pre-processing step for trajectory mining is trajectory segmentation, where a raw trajectory is divided into several meaningful consecutive sub-sequences. In this work, we propose an unsupervised trajectory segmentation algorithm, Sliding Window Segmentation (SWS), that processes an error signal generated by calculating the deviation of the middle point of an octal window from its imaginary interpolated version. This algorithm is flexible and can be applied to different domains by selecting an appropriate interpolation kernel. We examined our algorithm on three datasets of three different domains such as meteorology, fishing, and people moving in a big city. We also compared SWS with three other trajectory segmentation algorithms, namely GRASP-UTS, CB-SMoT, and SPD. Our experiments show that the proposed algorithm achieves the highest harmonic mean of purity and coverage for all datasets and explored algorithms with statistically significant differences.

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Acknowledgements

The authors would like to thank NSERC (Natural Sciences and Engineering Research Council of Canada) for financial support.

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Authors and Affiliations

  1. Institute for Big Data Analytics, Dalhousie University, 6050 University Ave., Halifax, NS, B3H 1W5, Canada

    Mohammad Etemad, Elham Etemad, Jordan Rose, Luis Torgo & Stan Matwin

  2. Department of Computer Science, Memorial University of Newfoundland, S.J. Crew Building, EN-2021, St. John’s, NL, A1B 3X5, Canada

    Amilcar Soares

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  1. Mohammad Etemad
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Correspondence to Mohammad Etemad.

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Etemad, M., Soares, A., Etemad, E. et al. SWS: an unsupervised trajectory segmentation algorithm based on change detection with interpolation kernels. Geoinformatica 25, 269–289 (2021). https://doi.org/10.1007/s10707-020-00408-9

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