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
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
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
Indexing Multidimensional Time-Series
The VLDB Journal — The International Journal on Very Large Data Bases
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
Unsupervised pattern mining from symbolic temporal data
ACM SIGKDD Explorations Newsletter - Special issue on data mining for health informatics
Mining relationships among interval-based events for classification
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
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
Temporal pattern mining in symbolic time point and time interval data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Distance measure for querying sequences of temporal intervals
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
Finding representative objects using link analysis ranking
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
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In several application domains, such as sign language, medicine, and sensor networks, events are not necessarily instantaneous but they can have a time duration. Sequences of interval-based events may contain useful domain knowledge; thus, searching, indexing, and mining such sequences is crucial. We introduce two distance measures for comparing sequences of interval-based events which can be used for several data mining tasks such as classification and clustering. The first measure maps each sequence of interval-based events to a set of vectors that hold information about all concurrent events. These sets are then compared using an existing dynamic programming method. The second method, called Artemis, finds correspondence between intervals by mapping the two sequences into a bipartite graph. Similarity is inferred by employing the Hungarian algorithm. In addition, we present a linear-time lowerbound for Artemis. The performance of both measures is tested on data from three domains: sign language, medicine, and sensor networks. Experiments show the superiority of Artemis in terms of robustness to high levels of artificially introduced noise.