# Wise Sliding Window Segmentation: A classification-aided approach for trajectory segmentation

arXiv: https://arxiv.org/abs/2003.10248 · HTML: https://mlsanity.com/paper/2003.10248/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vessels](https://mlsanity.com/paper/2207.05514/) (2022)
- [An unsupervised approach for semantic place annotation of trajectories based on the prior probability](https://mlsanity.com/paper/2204.09054/) (2022)
- [ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction in Machine Learning](https://mlsanity.com/paper/2401.05418/) (2023)

## Abstract (arXiv)

Large amounts of mobility data are being generated from many different sources, and several data mining methods have been proposed for this data. One of the most critical steps for trajectory data mining is segmentation. This task can be seen as a pre-processing step in which a trajectory is divided into several meaningful consecutive sub-sequences. This process is necessary because trajectory patterns may not hold in the entire trajectory but on trajectory parts. In this work, we propose a supervised trajectory segmentation algorithm, called Wise Sliding Window Segmentation (WS-II). It processes the trajectory coordinates to find behavioral changes in space and time, generating an error signal that is further used to train a binary classifier for segmenting trajectory data. This algorithm is flexible and can be used in different domains. We evaluate our method over three real datasets from different domains (meteorology, fishing, and individuals movements), and compare it with four other trajectory segmentation algorithms: OWS, GRASP-UTS, CB-SMoT, and SPD. We observed that the proposed algorithm achieves the highest performance for all datasets with statistically significant differences in terms of the harmonic mean of purity and coverage.