A Trajectory Segmentation Algorithm Based on Interpolation-based Change Detection Strategies

Trajectory mining is a research field which aims to provide fundamental insights into decision-making tasks related to moving objects. One of the fundamental pre-processing steps for trajectory mining is its segmentation, where a raw trajectory is divided into several meaningful consecutive sub-sequences. In this work, we propose an unsupervised trajectory segmentation algorithm named Octal Window Segmentation (OWS) that is based on the processing an error signal generated by measuring the deviation of a middle point of an octal window. The algorithm we propose is flexible and can be applied to different domains by selecting an appropriate interpolation kernel. We examined our algorithm on two datasets from different domains. The experiments show that the proposed algorithm achieved more than 93% of a crossvalidated harmonic mean of purity and coverage for two different datasets. We also show that statistically significantly higher results were obtained by OWS when compared with a baseline for unsupervised trajectory segmentation.