Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Mining, indexing, and querying historical spatiotemporal data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient mining of group patterns from user movement data
Data & Knowledge Engineering
Computing longest duration flocks in trajectory data
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
Discovery of convoys in trajectory databases
Proceedings of the VLDB Endowment
Swarm: mining relaxed temporal moving object clusters
Proceedings of the VLDB Endowment
Spatial and Spatio-temporal Data Mining
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
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One of the objectives of spatio-temporal data mining is to analyze moving object datasets to exploit interesting patterns. Traditionally, existing methods only focus on an unchanged group of moving objects during a time period. Thus, they cannot capture object moving trends which can be very useful for better understanding the natural moving behavior in various real world applications. In this paper, we present a novel concept of "time relaxed gradual trajectory pattern", denoted real-Gpattern, which captures the object movement tendency. Additionally, we also propose an efficient algorithm, called ClusterGrowth, designed to extract the complete set of all interesting maximal real-Gpatterns. Conducted experiments on real and large synthetic datasets demonstrate the effectiveness, parameter sensitiveness and efficiency of our methods.