Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
A data model and data structures for moving objects databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A foundation for representing and querying moving objects
ACM Transactions on Database Systems (TODS)
Maintaining knowledge about temporal intervals
Communications of the ACM
A fine grained heuristic to capture web navigation patterns
ACM SIGKDD Explorations Newsletter
A spatiotemporal database model and query language
Journal of Systems and Software
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Efficient Data Mining for Path Traversal Patterns
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
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Efficient Mining of Spatiotemporal Patterns
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Temporal Pattern Mining of Moving Objects for Location-Based Service
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Mining association rules on significant rare data using relative support
Journal of Systems and Software
Trajectories Mining for Traffic Condition Renewing
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Towards a taxonomy of movement patterns
Information Visualization
Exploring movement-similarity analysis of moving objects
SIGSPATIAL Special
Storing routes in socio-spatial networks and supporting social-based route recommendation
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Discovery of spatiotemporal patterns in mobile environment
APWeb'06 Proceedings of the 8th Asia-Pacific Web conference on Frontiers of WWW Research and Development
Continuous pattern mining using the FCPGrowth algorithm in trajectory data warehouses
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Experimental comparison of DWT and DFT for trajectory representation
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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The primary objective of location-based service (LBS) which is generally described as a mobile information service is to provide useful location aware information, at a minimum cost and resources, to its users. This functionality can be implemented through data mining techniques. However, since the conventional studies on data mining do not consider spatial and temporal aspects of data simultaneously, these techniques have limited application in studying the moving objects of LBS with respect to the spatial attributes that is changing over time. Defining individual users of LBS as moving objects, this paper proposes a new data mining technique and algorithms for identifying temporal patterns from series of locations of moving objects that have temporal and spatial dimensions. For this purpose, we use the spatial operation to generalize a location of moving point, applying time constraints between locations of moving objects to make valid moving sequences. Through the experiments, we show that our technique generates temporal patterns found in frequent moving sequences in efficient. Finally, the spatio-temporal technique proposed in this work is an innovative approach in providing knowledge applicable to improving the quality of LBS.