BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Trajectory clustering with mixtures of regression models
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A general probabilistic framework for clustering individuals and objects
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Efficient Retrieval of Similar Time Sequences Under Time Warping
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Detection of XML Structural Similarity
IEEE Transactions on Knowledge and Data Engineering
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Image Coding on Quincunx Lattice with Adaptive Lifting and Interpolation
DCC '07 Proceedings of the 2007 Data Compression Conference
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
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Discovery of convoys in trajectory databases
Proceedings of the VLDB Endowment
Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
Trajectory Outlier Detection: A Partition-and-Detect Framework
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
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
Compressing spatio-temporal trajectories
ISAAC'07 Proceedings of the 18th international conference on Algorithms and computation
Usability analysis of compression algorithms for position data streams
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
TrajPattern: mining sequential patterns from imprecise trajectories of mobile objects
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Incremental clustering for trajectories
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
Nonmaterialized motion information in transport networks
ICDT'05 Proceedings of the 10th international conference on Database Theory
Least squares quantization in PCM
IEEE Transactions on Information Theory
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.