An approach to discovering temporal association rules
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Knowledge Discovery from Series of Interval Events
Journal of Intelligent Information Systems - Data warehousing and knowledge discovery
Maintaining knowledge about temporal intervals
Communications of the ACM
Principles of data mining
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
Mining Temporal Features in Association Rules
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Discovery of Temporal Patterns. Learning Rules about the Qualitative Behaviour of Time Series
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Discovering Temporal Patterns for Interval-Based Events
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
Efficient Mining of Spatiotemporal Patterns
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Incremental Meta-Mining from Large Temporal Data Sets
ER '98 Proceedings of the Workshops on Data Warehousing and Data Mining: Advances in Database Technologies
Finding Informative Rules in Interval Sequences
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Mining maximal frequent intervals
Proceedings of the 2003 ACM symposium on Applied computing
Discovery of temporal patterns from process instances
Computers in Industry - Special issue: Process/workflow mining
Discovering Frequent Arrangements of Temporal Intervals
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Discovering Frequent Poly-Regions in DNA Sequences
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Mining Nonambiguous Temporal Patterns for Interval-Based Events
IEEE Transactions on Knowledge and Data Engineering
Data & Knowledge Engineering
Discovering Frequent Generalized Episodes When Events Persist for Different Durations
IEEE Transactions on Knowledge and Data Engineering
Mining frequent arrangements of temporal intervals
Knowledge and Information Systems
ARTEMIS: assessing the similarity of event-interval sequences
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Finding representative objects using link analysis ranking
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
Fast variable selection for memetracker phrases time series prediction
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
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Time series representations are not always rich enough to describe the temporal activity, for instance, when the context and the relations of the observed elements are of interest. Sequences of temporal intervals use such intervals as primitives in their representation, and allow focusing on the temporal relations of these elements. This is a useful representation of data across many domains. Searching, indexing, and mining such sequences is essential for domain experts in order to discover useful information out of them. In this paper, we formulate the problem of comparing sequences of temporal intervals and propose a novel distance measure. We discuss the properties of the measure and study its robustness in the domain of sign language. Experiments on real data show that the measure is robust in terms of retrieval accuracy even for high levels of artificially introduced distortion.