BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
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
Image Coding on Quincunx Lattice with Adaptive Lifting and Interpolation
DCC '07 Proceedings of the 2007 Data Compression Conference
Mining Frequent Trajectory Patterns for Activity Monitoring Using Radio Frequency Tag Arrays
PERCOM '07 Proceedings of the Fifth IEEE International Conference on Pervasive Computing and Communications
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering
Proceedings of the VLDB Endowment
Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
A Complete Framework for Clustering Trajectories
ICTAI '09 Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence
Trajectory Clustering via Effective Partitioning
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
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 amount of data is a challenging problem, 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 pose interesting challenges for their proper representation, 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 intriguing as we deal with a kind of data (trajectories) for which the order of elements is relevant. We propose a complete framework starting from data preparation task that allows us to make the mining step quite effective. Since the validation of data mining approaches has to be experimental we performed several tests on real world datasets that confirmed the efficiency and effectiveness of the proposed technique.