Fast discovery of association rules
Advances in knowledge discovery and data mining
Detecting change in categorical data: mining contrast sets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Transversing itemset lattices with statistical metric pruning
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Detecting Group Differences: Mining Contrast Sets
Data Mining and Knowledge Discovery
Correlated itemset mining in ROC space: a constraint programming approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining class-correlated patterns for sequence labeling
DS'10 Proceedings of the 13th international conference on Discovery science
Itemset mining: A constraint programming perspective
Artificial Intelligence
Towards a framework for inductive querying
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Efficient Search Methods for Statistical Dependency Rules
Fundamenta Informaticae - Machine Learning in Bioinformatics
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To mine databases in which examples are tagged with class labels, the minimum correlation constraint has been studied as an alternative to the minimum frequency constraint. We reformulate previous approaches and show that a minimum correlation constraint can be transformed into a disjunction of minimum frequency constraints. We prove that this observation extends to the multi-class χ2 correlation measure, and thus obtain an efficient new O(n) prune test. We illustrate how the relation between correlation measures and minimum support thresholds allows for the reuse of previously discovered pattern sets, thus avoiding unneccessary database evaluations. We conclude with experimental results to assess the effectivity of algorithms based on our observations.