Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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
TSP: Mining top-k closed sequential patterns
Knowledge and Information Systems
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Efficiently Mining Frequent Closed Partial Orders
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Frequent Closed Sequence Mining without Candidate Maintenance
IEEE Transactions on Knowledge and Data Engineering
A fast algorithm for finding frequent episodes in event streams
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient mining of frequent episodes from complex sequences
Information Systems
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Mining closed episodes from event sequences efficiently
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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Researchers have been endeavoring to discover concise sets of episode rules instead of complete sets in sequences. Existing approaches, however, are not able to process complex sequences and can not guarantee the accuracy of resulting sets due to the violation of anti-monotonicity of the frequency metric. In some real applications, episode rules need to be extracted from complex sequences in which multiple items may appear in a time slot. This paper investigates the discovery of concise episode rules in complex sequences. We define a concise representation called nonderivable episode rules and formularize the mining problem. Adopting a novel anti-monotonic frequency metric, we then develop a fast approach to discover non-derivable episode rules in complex sequences. Experimental results demonstrate that the utility of the proposed approach substantially reduces the number of rules and achieves fast processing.