Fast discovery of association rules
Advances in knowledge discovery and data mining
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences
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
ICDE '95 Proceedings of the Eleventh 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
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Mining Frequent Diamond Episodes from Event Sequences
MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
Mining Frequent Bipartite Episode from Event Sequences
DS '09 Proceedings of the 12th International Conference on Discovery Science
A simple characterization on serially constructible episodes
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Mining frequent k-partite episodes from event sequences
JSAI-isAI'09 Proceedings of the 2009 international conference on New frontiers in artificial intelligence
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In this paper, we introduce a sectorial episode of the form C↦r, where C is a set of events and r is an event. The sectorial episode C↦r means that every event of C is followed by an event r. Then, by formulating the support and the confidence of sectorial episodes, in this paper, we design the algorithm Sect to extract all of the sectorial episodes that are frequent and accurate from a given event sequence by traversing it just once. Finally, by applying the algorithm Sect to bacterial culture data, we extract sectorial episodes representing drug-resistant change.