As an important issue in the trajectory mining task, the trajectory clustering technique has attracted lots of the attention in the field of data mining. Trajectory clustering technique identifies the similar trajectories (or trajectory segments) and classifies them into the several clusters which can reveal the potential movement behaviors of nodes. At present, most of the existing trajectory clustering methods focus on some spatial properties of trajectories (such as geographic locations, movement directions), while the spatial-temporal properties (especially the combination of spatial distances and semantic distances) are ignored, and thus some vital information regarding the movement behaviors of nodes is probably lost in the trajectory clustering results. In this paper, we propose a Joint Spatial-Temporal Trajectory Clustering Method (JSTTCM), where some spatial-temporal properties of the trajectories are exploited to cluster the trajectory segments. Finally, the number of clusters and the silhouette coefficient are observed through simulations, and the results show that JSTTCM can cluster the trajectory segments appropriately.

中文翻译：

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基于时空特性的移动社交网络轨迹聚类方法

作为轨迹挖掘任务中的一个重要问题，轨迹聚类技术引起了数据挖掘领域的广泛关注。轨迹聚类技术识别相似的轨迹（或轨迹段）并将它们分类为几个可以揭示节点潜在运动行为的集群。目前，现有的轨迹聚类方法大多侧重于轨迹的一些空间特性（如地理位置、运动方向），而忽略了时空特性（尤其是空间距离和语义距离的组合），因此一些关于节点运动行为的重要信息可能会在轨迹聚类结果中丢失。在本文中，我们提出了一种联合时空轨迹聚类方法（JSTTCM），其中利用轨迹的一些时空特性来聚类轨迹段。最后，通过仿真观察聚类数和轮廓系数，结果表明JSTTCM可以对轨迹段进行适当的聚类。