Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
A technique for computer detection and correction of spelling errors
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On Similarity Queries for Time-Series Data: Constraint Specification and Implementation
CP '95 Proceedings of the First International Conference on Principles and Practice of Constraint Programming
Expressing and optimizing sequence queries in database systems
ACM Transactions on Database Systems (TODS)
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On computing the Hamming distance
Acta Cybernetica
Fast principal component analysis using fixed-point algorithm
Pattern Recognition Letters
Trajectory clustering: a partition-and-group framework
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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
Efficient pattern matching over event streams
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
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
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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 of extracting useful information from this particular type of data is crucial in many application scenarios such as vehicle traffic management, hand-off in cellular networks and supply chain management. In this paper, we address the issue of clustering spatial trajectories. In the context of trajectory data, this problem is even more challenging than in classical transactional relationships, 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.