BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery of convoys in trajectory databases
Proceedings of the VLDB Endowment
TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering
Proceedings of the VLDB Endowment
Trajectory Outlier Detection: A Partition-and-Detect Framework
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
TrajPattern: mining sequential patterns from imprecise trajectories of mobile objects
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Finding homogeneous groups in trajectory streams
Proceedings of the Third ACM SIGSPATIAL International Workshop on GeoStreaming
Effectively grouping trajectory streams
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
Sequential pattern mining from trajectory data
Proceedings of the 17th International Database Engineering & Applications Symposium
Dealing with trajectory streams by clustering and mathematical transforms
Journal of Intelligent Information Systems
| Hi-index | 0.00 |
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.