Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
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
Constraint-based mining of episode rules and optimal window sizes
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
A fast algorithm for finding frequent episodes in event streams
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Mining complex event patterns in computer networks
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
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Serial episode is a type of temporal frequent pattern in sequence data. In this paper we compare the performance of serial episode discovering algorithms. Many different algorithms have been proposed to discover different types of episodes for different applications. However, it is unclear which algorithm is more efficient for discovering different types of episodes. We compare Minepi and WinMiner which discover serial episodes defined by minimal occurrence of subsequence. We find Minepi cannot discover all minimal occurrences of serial episodes as the literature, which proposed it, claimed. We also propose an algorithm Ap-epi to discover minimal occurrences of serial episode, which is a complement of Minepi. We propose an algorithm NOE-WinMiner which discovers non-overlapping episodes and compare it with an existing algorithm. Extensive experiments demonstrate that Ap-epi outperforms Minepi(fixed) when the minimum support is large and NOE-WinMiner beats the existing algorithm which discovers non-overlapping episodes with constraints between the two adjacent events.