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.

轨迹挖掘的目的是提供与移动物体相关的决策任务的基本见识。轨迹挖掘的基本预处理步骤是轨迹分割，其中将原始轨迹分为几个有意义的连续子序列。在这项工作中，我们提出了一种无监督的轨迹分割算法，即滑动窗口分割（SWS），该算法处理通过计算八进制窗口的中点与其虚构内插版本的偏差而生成的误差信号。该算法非常灵活，可以通过选择适当的插值内核将其应用于不同的域。我们在三个不同领域的三个数据集上检查了我们的算法，例如气象，捕鱼和大城市中的人口流动。我们还将SWS与其他三个轨迹分割算法（即GRASP-UTS，CB-SMoT和SPD）进行了比较。我们的实验表明，所提出的算法在所有数据集上均达到了纯度和覆盖率的最高谐波均值，并且探索了具有统计学显着性差异的算法。