Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
FlowMiner: Finding Flow Patterns in Spatio-Temporal Databases
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
A framework for mining topological patterns in spatio-temporal databases
Proceedings of the 14th ACM international conference on Information and knowledge management
Mining mobile group patterns: a trajectory-based approach
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Mining generalized spatio-temporal patterns
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Interval-orientation patterns in spatio-temporal databases
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
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A spatial co-orientation pattern refers to objects that frequently occur with the same spatial orientation, e.g. left, right, below, etc., among images. In this paper, we introduce temporal co-orientation pattern mining which is the problem of temporal aspects of spatial co-orientation patterns. A temporal coorientation pattern represents how spatial co-orientation patterns change over time. Temporal co-orientation pattern mining is useful for discovering tactics from play sequences of sports video data, because the most tactic patterns of basketball competition are constituted of such spatial co-orientation patterns in time order. We propose the three-stage approach, which transforms the problem into sequential pattern mining, for mining temporal co-orientation patterns. We experimentally evaluate the performance of the proposed algorithm and analysis the effect of these stages.