Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Aggregation and comparison of trajectories
Proceedings of the 10th ACM international symposium on Advances in geographic information systems
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
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
Mining Surprising Patterns Using Temporal Description Length
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th 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
Discovering Temporal Relation Rules Mining from Interval Data
EurAsia-ICT '02 Proceedings of the First EurAsian Conference on Information and Communication Technology
Mining association rules on significant rare data using relative support
Journal of Systems and Software
Temporal moving pattern mining for location-based service
Journal of Systems and Software
Mining frequent closed patterns in pointset databases
Information Systems
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The converge of location-aware devices, GIS functionalities and the increasing accuracy and availability of positioning technologies pave the way to a range of new types of location-based services. The field of spatiotemporal data mining where relationships are defined by spatial and temporal aspect of data is encountering big challenges since the increased search space of knowledge. In this study, we aim to propose algorithms for mining spatiotemporal patterns in mobile environment. Moving patterns are generated utilizing two algorithms called All_MOP and Max_MOP. The first one mines all frequent patterns and the other discovers only maximal frequent patterns. Our approach is applicable to location-based services such as tourist service, traffic service, and so on.