  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.
