Incremental and interactive sequence mining
Proceedings of the eighth international conference on Information and knowledge management
Discovering Spatial Co-location Patterns: A Summary of Results
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
FlowMiner: Finding Flow Patterns in Spatio-Temporal Databases
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
A generalized framework for mining spatio-temporal patterns in scientific data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
International Journal of Computer Vision
A framework for mining topological patterns in spatio-temporal databases
Proceedings of the 14th ACM international conference on Information and knowledge management
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
A Framework for Mining Sequential Patterns from Spatio-Temporal Event Data Sets
IEEE Transactions on Knowledge and Data Engineering
Mining relationships among interval-based events for classification
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Direct Discriminative Pattern Mining for Effective Classification
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Mining temporal co-orientation pattern from spatio-temporal databases
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Can we apply projection based frequent pattern mining paradigm to spatial co-location mining?
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
A filter-and-refine approach to mine spatiotemporal co-occurrences
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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In this paper, we present a framework to discover a spatio-temporal relationship patterns. In contrast to previous work in this area, features are modeled as durative rather than instantaneous. Our method takes into account feature's duration to capture the temporal influence of a feature on other features in spatial neighborhood. We have developed an algorithm to discover a temporal-spatial feature interaction patterns, called the Interval-Orientation Patterns. Interval-Orientation pattern is a frequent sequence of features with annotation of temporal and directional relationships between every pairs of features. The proposed algorithm employs Hash-based joining technique to improve the efficiency. We also extend our approach to accommodate an incremental mining as updates in real world spatio-temporal databases are common. The incremental algorithm employs an optimization that is based on previously generated patterns to prune the non-promising candidates early. We evaluate our algorithms on synthetic dataset to demonstrate its efficiency and scalability. We also present the patterns identified from real world drought, vegetation and video action databases. We also show that the patterns discovered from video dataset can improve the classification accuracy of activity recognition.