Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
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
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
CoMine: Efficient Mining of Correlated Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining strongly associated rules
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
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Mutually associated pattern mining can find such type of patterns whose any two sub-patterns are associated. However, like frequent pattern mining, when the minimum association threshold is set to be low, it still generates a large number of mutually associated patterns. The huge number of patterns produced not only reduces the mining efficiency, but also makes it very difficult for a human user to analyze in order to identify interesting/useful ones. In this paper, a new task of maximal frequent mutually associated pattern mining is proposed, which can dramatically decrease the number of patterns produced without information loss due to the downward closure property of the association measure and meanwhile improve the mining efficiency. Experimental results show that maximal frequent mutually associated pattern mining is quite a necessary approach to lessening the number of results and increasing the performance of the algorithm. Also, experimental results show that the techniques developed are much effective especially for very large and dense datasets.