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 for Strong Negative Associations in a Large Database of Customer Transactions
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Mining Negative Association Rules
ISCC '02 Proceedings of the Seventh International Symposium on Computers and Communications (ISCC'02)
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
Mining positive and negative association rules: an approach for confined rules
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Efficient Search Methods for Statistical Dependency Rules
Fundamenta Informaticae - Machine Learning in Bioinformatics
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Typically association rule mining only considers positive frequent itemsets in rule generation, where rules involving only the presence of items are generated. In this paper we consider the complementary problem of negative association rule mining, which generates rules describing the absence of itemsets from transactions. We describe a new approach called MINR (Mining Interesting Negative Rules) to efficiently find all interesting negative association rules. Here we only consider the presence or absence of itemsets that are strongly associated. Our approach does not require a user defined support threshold, and is based on pruning items that occur together by coincidence. For every individual itemset we calculate two custom thresholds based on their support: the positive and negative chance thresholds. We compared our implementation against Pearson φ correlation.