Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
GeoMiner: a system prototype for spatial data mining
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovering Temporal Patterns in Multiple Granularities
TSDM '00 Proceedings of the First International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining-Revised Papers
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Spatial Data Mining: A Database Approach
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
Efficient Mining of Spatiotemporal Patterns
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Research Challenges in Location-Enabled M-Services
MDM '02 Proceedings of the Third International Conference on Mobile Data Management
Multidimensional data modeling for location-based services
The VLDB Journal — The International Journal on Very Large Data Bases
Mining, indexing, and querying historical spatiotemporal data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Highly scalable trip grouping for large-scale collective transportation systems
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Mining Frequent Trajectories of Moving Objects for Location Prediction
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Spatio-temporal mining for power load forecasting in GIS-AMR load analysis model
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
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Recent advances in communication and information technology, such as the increasing accuracy of GPS technology and the miniaturization of wireless communication devices pave the road for Location–Based Services (LBS). To achieve high quality for such services, spatio–temporal data mining techniques are needed. In this paper, we describe experiences with spatio–temporal rule mining in a Danish data mining company. First, a number of real world spatio–temporal data sets are described, leading to a taxonomy of spatio–temporal data. Second, the paper describes a general methodology that transforms the spatio–temporal rule mining task to the traditional market basket analysis task and applies it to the described data sets, enabling traditional association rule mining methods to discover spatio–temporal rules for LBS. Finally, unique issues in spatio–temporal rule mining are identified and discussed.