Reverse search for enumeration
Discrete Applied Mathematics - Special volume: first international colloquium on graphs and optimization (GOI), 1992
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: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Discovering Frequent Closed Partial Orders from Strings
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
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
An efficient algorithm for enumerating pseudo cliques
ISAAC'07 Proceedings of the 18th international conference on Algorithms and computation
Efficient algorithms for mining frequent and closed patterns from semi-structured data
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
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 sectorial episodes from event sequences
DS'06 Proceedings of the 9th international conference on Discovery Science
A polynomial space and polynomial delay algorithm for enumeration of maximal motifs in a sequence
ISAAC'05 Proceedings of the 16th international conference on Algorithms and Computation
EVIS: a fast and scalable episode matching engine for massively parallel data streams
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
| Hi-index | 0.00 |
In this paper, we introduce the class of k-partite episodes, which are time-series patterns of the form 〈A1,..., Ak〉 for sets Ai (1 ≤ i ≤ k) of events meaning that, in an input event sequence, every event of Ai is followed by every event of Ai+1 for every 1 ≤ i k. Then, we present a backtracking algorithm KPAR and its modification KPAR2 that find all of the frequent k-partite episodes from an input event sequence without duplication. By theoretical analysis, we show that these two algorithms run in polynomial delay and polynomial space in total input size.