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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 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 clustering of spatial trajectories. In the context of trajectory data, this problem is even more challenging than in the classical transactions, 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.