Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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ICDE '98 Proceedings of the Fourteenth 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
Discovering itemset interactions
ACSC '09 Proceedings of the Thirty-Second Australasian Conference on Computer Science - Volume 91
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SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
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Standard association rules encapsulate the positive relationship between two sets of items: the presence of X is a good predictor for the simultaneous presence of Y . We argue that the absence of an association rule conveys valuable information as well. Dissociation rules are rules that capture the negative relationship between two sets of items: the presence of X and z is not a good predictor for the presence of Y . We developed a representation for augmenting standard association rules with dissociation information, and presented some experimental results suggesting that such augmented rules can improve the quality of the associations obtained, both in terms of rule accuracy and in terms of using these rules as a guide to making decisions.