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
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining for Strong Negative Associations in a Large Database of Customer Transactions
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Indirect Association: Mining Higher Order Dependencies in Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery of indirect association and its applications
Discovery of indirect association and its applications
Mining Indirect Association Rules for Web Recommendation
International Journal of Applied Mathematics and Computer Science
Semantic analysis of association rules via item response theory
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
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A large database, such as POS data, could give us many insights about customer behavior. Many techniques and measures have been proposed to extract interesting rules. As the study of Association rule mining has proceeded, the rules about items that are not bought together at the same transaction have been regarded as important. Although this concept, Negative Association rule mining, is quite useful, it is difficult for the user to analyze the interestingness of Negative Association rules because we would get them too many. To settle this issue, Indirect Association rule mining has proposed. In this paper, we propose a new framework of Indirect Association rule via a mediator and a new measure μ based on measures PA and PD due to Zhang to mine Negative Association rules effectively without the domain knowledge. The μ measure has the advantage over the IS measure that is proposed with the first framework of Indirect Association rule mining, and satisfies all of the well-known properties for a good measure. Finally, we are going to analyze the retail data and present interpretations for derived Indirect Association rules.