How to use "classical" tree mining algorithms to find complex spatio-temporal patterns?
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
SSCP: mining statistically significant co-location patterns
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
The pattern next door: towards spatio-sequential pattern discovery
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Graph mining for object tracking in videos
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Co-occurrence prediction in a large location-based social network
Frontiers of Computer Science: Selected Publications from Chinese Universities
Motif discovery in spatial trajectories using grammar inference
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
A multiple window-based co-location pattern mining approach for various types of spatial data
International Journal of Computer Applications in Technology
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Mixed-drove spatio-temporal co-occurrence patterns (MDCOPs) represent subsets of two or more different object-types whose instances are often located in spatial and temporal proximity. Discovering MDCOPs is an important problem with many applications such as identifying tactics in battlefields, games, and predator-prey interactions. However, mining MDCOPs is computationally very expensive because the interest measures are computationally complex, datasets are larger due to the archival history, and the set of candidate patterns is exponential in the number of object-types. We propose a monotonic composite interest measure for discovering MDCOPs and novel MDCOP mining algorithms. Analytical results show that the proposed algorithms are correct and complete. Experimental results also show that the proposed methods are computationally more efficient than naive alternatives.