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

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 propo...

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Bibliographic Details
Published in:GeoInformatica 2021-04, Vol.25 (2), p.269-289
Main Authors: Etemad, Mohammad, Soares, Amilcar, Etemad, Elham, Rose, Jordan, Torgo, Luis, Matwin, Stan
Format: Article
Language:English
Subjects:
Algorithms
Computer Science
Data Structures and Information Theory
Datasets
Decision making
Domains
Earth and Environmental Science
Error signals
Fishing
Geographical Information Systems/Cartography
Geography
Information Storage and Retrieval
Interpolation
Kernels
Meteorology
Multimedia Information Systems
Segmentation
Signal processing
Statistical analysis
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Summary: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.
ISSN:1384-6175
1573-7624
DOI:10.1007/s10707-020-00408-9