BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering
Proceedings of the VLDB Endowment
Incremental clustering for trajectories
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
IEEE Transactions on Image Processing
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Trajectory data streams are huge amounts of data pertaining to time and position of moving objects. They are continuously generated by different sources exploiting a wide variety of technologies (e.g., RFID tags, GPS, GSM networks). 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. Moreover, spatial data streams poses interesting challenges both for their proper definition and acquisition, thus making the mining process harder than for classical point data. In this paper, we address the problem of trajectory data streams clustering, that revealed really challenging as we deal with data (trajectories) for which the order of elements is relevant.