FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Mining frequent patterns by pattern-growth: methodology and implications
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns with Regular Expression Constraints
IEEE Transactions on Knowledge and Data Engineering
Efficient Mining of Intertransaction Association Rules
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
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Mining Partially Periodic Event Patterns with Unknown Periods
Proceedings of the 17th International Conference on Data Engineering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Asynchronous Periodic Patterns in Time Series Data
IEEE Transactions on Knowledge and Data Engineering
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth
ICDE '01 Proceedings of the 17th International Conference on Data Engineering
Reliable Detection of Episodes in Event Sequences
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Frequent Episode Rules for Internet Anomaly Detection
NCA '04 Proceedings of the Network Computing and Applications, Third IEEE International Symposium
Detection of Significant Sets of Episodes in Event Sequences
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
COCOA: compressed continuity analysis for temporal databases
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
SMCA: A General Model for Mining Asynchronous Periodic Patterns in Temporal Databases
IEEE Transactions on Knowledge and Data Engineering
Mining frequent episodes for relating financial events and stock trends
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Automatic assertion extraction via sequential data mining of simulation traces
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
Rule generation for categorical time series with Markov assumptions
Statistics and Computing
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
Fast mining of non-derivable episode rules in complex sequences
MDAI'11 Proceedings of the 8th international conference on Modeling decisions for artificial intelligence
Mining association rules from time series to explain failures in a hot-dip galvanizing steel line
Computers and Industrial Engineering
Improved artificial negative event generation to enhance process event logs
CAiSE'12 Proceedings of the 24th international conference on Advanced Information Systems Engineering
Mining high utility episodes in complex event sequences
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Editorial: Pattern-growth based frequent serial episode discovery
Data & Knowledge Engineering
Mining Therapeutic Patterns from Clinical Data for Juvenile Diabetes
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
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Discovering patterns with great significance is an important problem in data mining discipline. An episode is defined to be a partially ordered set of events for consecutive and fixed-time intervals in a sequence. Most of previous studies on episodes consider only frequent episodes in a sequence of events (called simple sequence). In real world, we may find a set of events at each time slot in terms of various intervals (hours, days, weeks, etc.). We refer to such sequences as complex sequences. Mining frequent episodes in complex sequences has more extensive applications than that in simple sequences. In this paper, we discuss the problem on mining frequent episodes in a complex sequence. We extend previous algorithm MINEPI to MINEPI+ for episode mining from complex sequences. Furthermore, a memory-anchored algorithm called EMMA is introduced for the mining task. Experimental evaluation on both real-world and synthetic data sets shows that EMMA is more efficient than MINEPI+.