Global partial orders from sequential data
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Knowledge Discovery from Telecommunication Network Alarm Databases
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Discovering Frequent Episodes and Learning Hidden Markov Models: A Formal Connection
IEEE Transactions on Knowledge and Data Engineering
Discovering Frequent Closed Partial Orders from Strings
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
Exploiting Frequent Episodes in Weighted Suffix Tree to Improve Intrusion Detection System
AINAW '08 Proceedings of the 22nd International Conference on Advanced Information Networking and Applications - Workshops
Stream prediction using a generative model based on frequent episodes in event sequences
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Frequent Generalized Episodes When Events Persist for Different Durations
IEEE Transactions on Knowledge and Data Engineering
Significance of Episodes Based on Minimal Windows
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on 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
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
VOGUE: a novel variable order-gap state machine for modeling sequences
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
The long and the short of it: summarising event sequences with serial episodes
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Editorial: Pattern-growth based frequent serial episode discovery
Data & Knowledge Engineering
Discovering episodes with compact minimal windows
Data Mining and Knowledge Discovery
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Frequent episode discovery is a popular framework for temporal pattern discovery in event streams. An episode is a partially ordered set of nodes with each node associated with an event type. Currently algorithms exist for episode discovery only when the associated partial order is total order (serial episode) or trivial (parallel episode). In this paper, we propose efficient algorithms for discovering frequent episodes with unrestricted partial orders when the associated event-types are unique. These algorithms can be easily specialized to discover only serial or parallel episodes. Also, the algorithms are flexible enough to be specialized for mining in the space of certain interesting subclasses of partial orders. We point out that frequency alone is not a sufficient measure of interestingness in the context of partial order mining. We propose a new interestingness measure for episodes with unrestricted partial orders which, when used along with frequency, results in an efficient scheme of data mining. Simulations are presented to demonstrate the effectiveness of our algorithms.