Mining asynchronous periodic patterns in time series data
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
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
Generating Network-Based Moving Objects
SSDBM '00 Proceedings of the 12th International Conference on Scientific and Statistical Database Management
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th 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
A data mining approach for location prediction in mobile environments
Data & Knowledge Engineering
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
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With the recent development of LBS(Location Based Service) and Telematics, the use of spatio-temporal data mining which extracts useful knowledge such as movement patterns of moving objects gets increasing. However, the existing movement pattern extraction methods including STPMine1 and STPMine2 create lots of candidate movement patterns when the minimum support is low. As a result of that, the performance of time and space is sharply increased as a weak point. Therefore, in this paper, we suggest the STMPE (Spatio-Temporal Movement Pattern Extraction) algorithm in order to efficiently extract movement patterns of moving objects from the large capacity of spatio-temporal data. The STMPE algorithm generalizes spatio-temporal data and minimizes the use of memory. Because it produces and maintains short-term movement patterns, the frequency of database scan can be minimized. Actually, the STMPE algorithm was improved twice to 10 times better than STPMine1 and STPMine2 from the result of performance evaluation.