A Framework for Trajectory Clustering

Abstract

The increasing availability of huge amounts of “thin” data, i.e. data pertaining to time and positions generated by different sources with a wide variety of technologies (e.g., RFID tags, GPS, GSM networks) leads to large spatio-temporal data collections. Mining such amounts of data is challenging, since the possibility of extracting useful information from this particular type of data is crucial in many application scenarios such as vehicle traffic management, hand-off in cellular networks and supply chain management. In this paper, we address the issue of clustering spatial trajectories. In the context of trajectory data, this problem is even more challenging than in classical transactional relationships, as here we deal with data (trajectories) in which the order of items is relevant. We propose a novel approach based on a suitable regioning strategy and an efficient clustering technique based on edit distance. Experiments performed on real world datasets have confirmed the efficiency and effectiveness of the proposed techniques.