Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and 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 Search of Reliable Exceptions
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and 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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Positive correlation mining can find such type of patterns, “the conditional probability that a customer purchasing A is likely to also purchase B is not only great enough, but also significantly greater than the probability that a customer purchases only B.” However, there often exist many independence relationships between items in a correlated pattern due to the definition of a correlated pattern. Therefore, we mine mutually and positively correlated patterns, whose any two sub-patterns are both associated and positively correlated. A new correlation interestingness measure is proposed for rationally evaluating the correlation degree. In order to improve the mining efficiency, we combine association with correlation and use not only the correlation measure but also the association measure in the mining process. Our experimental results show that mutually and positively correlated pattern mining is a good approach to discovering patterns which can reflect both association and positive correlation relationships between items at the same time. Meanwhile, our experimental results show that the mining combined association with correlation is quite a valid method to decrease the execution time.