Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining asynchronous periodic patterns in time series data
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Infominer: mining surprising periodic patterns
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Asynchronous Periodic Patterns in Time Series Data
IEEE Transactions on Knowledge and Data Engineering
InfoMiner+: Mining Partial Periodic Patterns with Gap Penalties
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
SMCA: A General Model for Mining Asynchronous Periodic Patterns in Temporal Databases
IEEE Transactions on Knowledge and Data Engineering
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The periodic pattern mining is to discover valid periodic patterns in a time-related dataset. Previous studies mostly concern the synchronous periodic patterns. There are many methods for mining periodic patterns proposed in literature. Nevertheless, asynchronous periodic pattern mining gradually receives more and more attention recently. In this paper, we propose an efficient linked structure and the OEOP algorithm to discover all kinds of valid segments in each single event sequence. Then, refer to the general model of asynchronous periodic pattern mining proposed by Huang and Chang, we combine these valid segments found by OEOP into 1-patterns with multiple events, multiple patterns with multiple events and asynchronous periodic patterns. Besides, we implement these algorithms on two real datasets. The experimental results show that these algorithms have the good performance and scalability.