Trajectory Clustering via Effective Partitioning

The increasing availability of huge amounts of data pertaining to time and positions generated by different sources using a wide variety of technologies (e.g., RFID tags, GPS, GSM networks) leads to large spatial data collections. Mining such amounts of data is challenging, since the possibility to extract useful information from this peculiar kind of data is crucial in many application scenarios such as vehicle traffic management, hand-off in cellular networks, supply chain management. In this paper, we address the problem of clustering spatial trajectories. In the context of trajectory data, clustering is really challenging as we deal with data (trajectories) for which the order of elements is relevant. We propose a novel approach based on a suitable regioning strategy and an efficient and effective clustering technique based on a proper metric. Finally, we performed several tests on real world datasets that confirmed the efficiency and effectiveness of the proposed techniques.