Temporal reasoning based on semi-intervals
Artificial Intelligence
ACM Transactions on Information Systems (TOIS)
Maintaining knowledge about temporal intervals
Communications of the ACM
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
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
Point-Versus Interval-Based Temporal Data Models
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Association Rules & Evolution in Time
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Fast Discovery of Sequential Patterns by Memory Indexing
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Discovering Calendar-Based Temporal Association Rules
TIME '01 Proceedings of the Eighth International Symposium on Temporal Representation and Reasoning (TIME'01)
Linear Temporal Sequences and Their Interpretation Using Midpoint Relationships
IEEE Transactions on Knowledge and Data Engineering
Efficient mining of understandable patterns from multivariate interval time series
Data Mining and Knowledge Discovery
Unsupervised pattern mining from symbolic temporal data
ACM SIGKDD Explorations Newsletter - Special issue on data mining for health informatics
Mining frequent arrangements of temporal intervals
Knowledge and Information Systems
An approach to discovering multi-temporal patterns and its application to financial databases
Information Sciences: an International Journal
TOD: Temporal outlier detection by using quasi-functional temporal dependencies
Data & Knowledge Engineering
An efficient algorithm for mining time interval-based patterns in large database
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
CIMMACS '10 Proceedings of the 9th WSEAS international conference on computational intelligence, man-machine systems and cybernetics
Temporal association rules mining: a heuristic methodology applied to time series databases (TSDBs)
CIMMACS '10 Proceedings of the 9th WSEAS international conference on computational intelligence, man-machine systems and cybernetics
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
Distance measure for querying sequences of temporal intervals
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
Mining association rules from time series to explain failures in a hot-dip galvanizing steel line
Computers and Industrial Engineering
Activity recognition with finite state machines
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Mining recent temporal patterns for event detection in multivariate time series data
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
A temporal pattern mining approach for classifying electronic health record data
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
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Temporal association rule mining promises the ability to discover time-dependent correlations or patterns between events in large volumes of data. To date, most temporal data mining research has focused on events existing at a point in time rather than over a temporal interval. In comparison to static rules, mining with respect to time points provides semantically richer rules. However, accommodating temporal intervals offers rules that are richer still. In this paper we outline a new algorithm, ARMADA, to discover frequent temporal patterns and to generate richer interval-based temporal association rules. In addition, we introduce a maximum gap time constraint that can be used to get rid of insignificant patterns and rules so that the number of generated patterns and rules can be reduced. Synthetic datasets are utilized to assess the performance of the algorithm.