MAT-Tree: A Tree-Based Method for Multiple Aspect Trajectory Clustering.

Multiple aspect trajectory is a relevant concept that enables mining interesting patterns and behaviors of moving objects for different applications. This new way of looking at trajectories includes a semantic dimension, which presents the notion of aspects that are relevant facts of the real world that add more meaning to spatio-temporal data. Given the inherent complexity of this new type of data, the development of new data mining methods is needed. Despite some works have already focused on multiple aspect trajectory classification, few have focused on clustering. Although the literature presents several raw trajectory clustering algorithms, they do not deal with the heterogeneity of the semantic dimension. In this paper, we propose a novel hierarchical clustering algorithm for multiple aspect trajectories using a decision tree structure that chooses the best aspect to branch and group the most similar trajectories according to different criteria. We ran experiments using a well-known benchmark dataset extracted from a location-based social network and compared our clustering results with a state-of-the-art clustering approach over different internal and external validation metrics. As a result, we show that the proposed method outperformed the baseline, where it revealed a formation of more cohesive and homogeneous clusters in 88% of the clusters, being five times more precise according to the external metrics.