Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Sequence mining in categorical domains: incorporating constraints
Proceedings of the ninth international conference on Information and knowledge management
Maintaining knowledge about temporal intervals
Communications of the ACM
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
Aggregation and comparison of trajectories
Proceedings of the 10th ACM international symposium on Advances in geographic information systems
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Capturing the Uncertainty of Moving-Object Representations
SSD '99 Proceedings of the 6th 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
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
Modeling, Storing, and Mining Moving Object Databases
IDEAS '04 Proceedings of the International Database Engineering and Applications Symposium
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A data mining approach for location prediction in mobile environments
Data & Knowledge Engineering
A method for predicting future location of mobile user for location-based services system
Computers and Industrial Engineering
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The mobile wireless market has been attracting many customers. Technically, the paradigm of anytime-anywhere connectivity raises previously unthinkable challenges, including the management of million of mobile customers, their profiles, the profiles-based selective information dissemination, and server-side computing infrastructure design issues to support such a large pool of users automatically and intelligently. In this paper, we propose a data mining technique for discovering frequent behavioral patterns from a collection of trajectories gathered by Global Positioning System. Although the search space for spatiotemporal knowledge is extremely challenging, imposing spatial and temporal constraints on spatiotemporal sequences makes the computation feasible. Specifically, the mined patterns are incorporated with synthetic constraints, namely spatiotemporal sequence length restriction, minimum and maximum timing gap between events, time window of occurrence of the whole pattern, inclusion or exclusion event constraints, and frequent movement patterns predictive of one ore more classes. The algorithm for mining all frequent constrained patterns is named cAllMOP. Moreover, to control the density of pattern regions a clustering algorithm is exploited. The proposed method is efficient and scalable. Its efficiency is better than that of the previous algorithms AllMOP and GSP with respect to the compactness of discovered knowledge, execution time, and memory requirement.