Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Maintaining knowledge about temporal intervals
Communications of the ACM
The TSQL2 Temporal Query Language
The TSQL2 Temporal Query Language
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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
Distance measure for querying sequences of temporal intervals
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
Finding all minimal infrequent multi-dimensional intervals
LATIN'06 Proceedings of the 7th Latin American conference on Theoretical Informatics
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Many real world data are associated with intervals of time or distance. Mining frequent intervals from such data allows the users to group transactions with similar behavior together. Previous work only focuses on the problem of mining frequent intervals in a discrete domain. This paper first proposes the notion of maximal frequent intervals, which are superior to frequent intervals in many perspectives, and then provides a method for mining maximal frequent intervals in either a discrete domain or a continuous domain. Experimental results indicate that our method outperforms previous method for mining frequent intervals, and improves the runtime by two orders of magnitude for databases with a large number of distinct endpoints.