Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Integration with spatiotemporal relationship operators in SQL
Proceedings of the 6th ACM international symposium on Advances in geographic information systems
An approach to discovering temporal association rules
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Maintaining knowledge about temporal intervals
Communications of the ACM
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
On the Discovery of Interesting Patterns in Association Rules
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A Framework for Temporal Data Mining
DEXA '98 Proceedings of the 9th 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
Discovery of spatiotemporal patterns in mobile environment
APWeb'06 Proceedings of the 8th Asia-Pacific Web conference on Frontiers of WWW Research and Development
Discovery of temporal frequent patterns using TFP-Tree
WAIM '06 Proceedings of the 7th international conference on Advances in Web-Age Information Management
An iterative method for mining frequent temporal patterns
EUROCAST'05 Proceedings of the 10th international conference on Computer Aided Systems Theory
Temporal data mining with up-to-date pattern trees
Expert Systems with Applications: An International Journal
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In this paper, we propose a new data mining technique that can address the temporal relation rules of temporal interval data by using Allen's theory. We present two new algorithms for discovering temporal relationships: one is to preprocess an algorithm for the generalization of temporal interval data and to transform timestamp data into temporal interval data; and the other is to use a temporal relation algorithm for mining temporal relation rules and to discover the rules from temporal interval data. This technique can provide more useful knowledge in comparison with other conventional data mining techniques.