Probabilistic counting algorithms for data base applications
Journal of Computer and System Sciences
An approach to discovering temporal association rules
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Discovering calendar-based temporal association rules
Data & Knowledge Engineering - Special issue: Temporal representation and reasoning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovering Spatial Co-location Patterns: A Summary of Results
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Efficient Mining of Spatiotemporal Patterns
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Mining confident co-location rules without a support threshold
Proceedings of the 2003 ACM symposium on Applied computing
Spatio-Temporal Aggregation Using Sketches
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Mining, indexing, and querying historical spatiotemporal data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
FlowMiner: Finding Flow Patterns in Spatio-Temporal Databases
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Computing longest duration flocks in trajectory data
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
Dimensionality reduction for long duration and complex spatio-temporal queries
Proceedings of the 2007 ACM symposium on Applied computing
Dynamic modeling of trajectory patterns using data mining and reverse engineering
ER '07 Tutorials, posters, panels and industrial contributions at the 26th international conference on Conceptual modeling - Volume 83
Computational Geometry: Theory and Applications
Engineering Contextual Information for Pervasive Multiagent Systems
Engineering Environment-Mediated Multi-Agent Systems
Detecting single file movement
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Measuring serendipity: connecting people, locations and interests in a mobile 3G network
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
ISAAC'07 Proceedings of the 18th international conference on Algorithms and computation
Detecting movement patterns with wireless sensor networks: application to bird behavior
Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia
Mining trajectory patterns using hidden Markov models
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Context Based Positive and Negative Spatio-Temporal Association Rule Mining
Knowledge-Based Systems
When and where next: individual mobility prediction
Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems
Discovering hot topics from geo-tagged video
Neurocomputing
Algorithms for hotspot computation on trajectory data
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Anytime algorithms for mining groups with maximum coverage
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
Incremental Frequent Route Based Trajectory Prediction
Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Computational Transportation Science
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As mobile devices proliferate and networks become more location-aware, the corresponding growth in spatio-temporal data will demand analysis techniques to mine patterns that take into account the semantics of such data. Association Rule Mining has been one of the more extensively studied data mining techniques, but it considers discrete transactional data (supermarket or sequential). Most attempts to apply this technique to spatial-temporal domains maps the data to transactions, thus losing the spatio-temporal characteristics. We provide a comprehensive definition of spatio-temporal association rules (STARs) that describe how objects move between regions over time. We define support in the spatio-temporal domain to effectively deal with the semantics of such data. We also introduce other patterns that are useful for mobility data; stationary regions and high traffic regions. The latter consists of sources, sinks and thoroughfares. These patterns describe important temporal characteristics of regions and we show that they can be considered as special STARs. We provide efficient algorithms to find these patterns by exploiting several pruning properties.