New probabilistic interest measures for association rules
Intelligent Data Analysis
Improved approaches to mine rare association rules in transactional databases
Proceedings of the Fourth SIGMOD PhD Workshop on Innovative Database Research
Proceedings of the 14th International Conference on Extending Database Technology
The arules R-Package Ecosystem: Analyzing Interesting Patterns from Large Transaction Data Sets
The Journal of Machine Learning Research
Mining rare association rules in the datasets with widely varying items' frequencies
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Detecting stealthy backdoors with association rule mining
IFIP'12 Proceedings of the 11th international IFIP TC 6 conference on Networking - Volume Part II
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Mining frequent itemsets is a popular method for finding associated items in databases. For this method, support, the co-occurrence frequency of the items which form an association, is used as the primary indicator of the associations's significance. A single, user-specified support threshold is used to decided if associations should be further investigated. Support has some known problems with rare items, favors shorter itemsets and sometimes produces misleading associations.In this paper we develop a novel model-based frequency constraint as an alternative to a single, user-specified minimum support. The constraint utilizes knowledge of the process generating transaction data by applying a simple stochastic mixture model (the NB model) which allows for transaction data's typically highly skewed item frequency distribution. A user-specified precision threshold is used together with the model to find local frequency thresholds for groups of itemsets. Based on the constraint we develop the notion of NB-frequent itemsets and adapt a mining algorithm to find all NB-frequent itemsets in a database. In experiments with publicly available transaction databases we show that the new constraint provides improvements over a single minimum support threshold and that the precision threshold is more robust and easier to set and interpret by the user.