The temporal query language TQuel
ACM Transactions on Database Systems (TODS)
A consensus glossary of temporal database concepts
ACM SIGMOD Record
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
High-Dimensional Similarity Joins
IEEE Transactions on Knowledge and Data Engineering
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
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
Mining Surprising Patterns Using Temporal Description Length
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 Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th 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
Temporal Pattern Mining of Moving Objects for Location-Based Service
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
Design and implementation of spatiotemporal database query processing system
Journal of Systems and Software
Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules
IEEE Transactions on Knowledge and Data Engineering
Mining association rules on significant rare data using relative support
Journal of Systems and Software
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Efficient calendar based temporal association rule
ACM SIGMOD Record
Discovering Frequent Episodes and Learning Hidden Markov Models: A Formal Connection
IEEE Transactions on Knowledge and Data Engineering
Discovering Frequent Arrangements of Temporal Intervals
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Journal of Intelligent Information Systems
Finding the most unusual time series subsequence: algorithms and applications
Knowledge and Information Systems
A scalable algorithm for mining maximal frequent sequences using a sample
Knowledge and Information Systems
Discovering Frequent Generalized Episodes When Events Persist for Different Durations
IEEE Transactions on Knowledge and Data Engineering
Mining temporal patterns from sequence database of interval-based events
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Mining expressive temporal associations from complex data
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
A geometric approach to the problem of reconstruction of the sample behavior in hidden dimensions
Pattern Recognition and Image Analysis
| Hi-index | 0.00 |
Temporal data mining is still one of important research topic since there are application areas that need knowledge from temporal data such as sequential patterns, similar time sequences, cyclic and temporal association rules, and so on. Although there are many studies for temporal data mining, they do not deal with discovering knowledge from temporal interval data such as patient histories, purchaser histories, and web logs etc. We propose a new temporal data mining technique that can extract temporal interval relation rules from temporal interval data by using Allen's theory: a preprocessing algorithm designed for the generalization of temporal interval data and a temporal relation algorithm for mining temporal relation rules from the generalized temporal interval data. This technique can provide more useful knowledge in comparison with conventional data mining techniques.