Maintaining knowledge about temporal intervals
Communications of the ACM
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Discovering Temporal Patterns for Interval-Based Events
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
Mining sequences with temporal annotations
Proceedings of the 2006 ACM symposium on Applied computing
Constraint-based sequential pattern mining: the pattern-growth methods
Journal of Intelligent Information Systems
Mining Nonambiguous Temporal Patterns for Interval-Based Events
IEEE Transactions on Knowledge and Data Engineering
A classification method based on subspace clustering and association rules
New Generation Computing
Dynamic clustering of interval data using a Wasserstein-based distance
Pattern Recognition Letters
Efficient mining of sequential patterns with time constraints: Reducing the combinations
Expert Systems with Applications: An International Journal
Mining Temporal Patterns with Quantitative Intervals
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Discovery of Quantitative Sequential Patterns from Event Sequences
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
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Most of the sequential patterns extraction methods proposed so far deal with patterns composed of events linked by temporal relationships based on simple precedence between instants. In many real situations, some quantitative information about event duration or inter-event delay is necessary to discriminate phenomena. We propose the algorithm QTIPrefixSpan for extracting temporal patterns composed of events to which temporal intervals describing their position in time and their duration are associated. It extends algorithm PrefixSpan with a multi-dimensional interval clustering step for extracting the representative temporal intervals associated to events in patterns. Experiments on simulated data show that our algorithm is efficient for extracting precise patterns even in noisy contexts and that it improves the performance of a former algorithm which used a clustering method based on the EM algorithm.