Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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
Can we push more constraints into frequent pattern mining?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining confident rules without support requirement
Proceedings of the tenth international conference on Information and knowledge management
Finding Interesting Associations without Support Pruning
IEEE Transactions on Knowledge and Data Engineering
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Mining Strong Affinity Association Patterns in Data Sets with Skewed Support Distribution
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Selecting the right objective measure for association analysis
Information Systems - Knowledge discovery and data mining (KDD 2002)
Mining risk patterns in medical data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
IEEE Transactions on Knowledge and Data Engineering
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
A Unified View of Objective Interestingness Measures
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Mining non-coincidental rules without a user defined support threshold
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Pushing tougher constraints in frequent pattern mining
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A robustness measure of association rules
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Mining classification rules without support: an anti-monotone property of Jaccard measure
DS'11 Proceedings of the 14th international conference on Discovery science
Optimonotone Measures For Optimal Rule Discovery
Computational Intelligence
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Many studies have shown the limits of support/confidence framework used in Apriori-like algorithms to mine association rules. There are a lot of efficient implementations based on the antimonotony property of the support but candidate set generation is still costly. In addition many rules are uninteresting or redundant and one can miss interesting rules like nuggets. One solution is to get rid of frequent itemset mining and to focus as soon as possible on interesting rules. For that purpose algorithmic properties were first studied, especially for the confidence. They allow all confidence rules to be found without a preliminary support pruning. More recently, in the case of class association rules, the concept of optimal rules gave a pruning strategy compatible with more measures. However, all these properties have been demonstrated for a limited number of interestingness measures. We present a new formal framework which allows us to make the link between analytic and algorithmic properties of the measures. We apply this framework to optimal rules, and we demonstrate a necessary and sufficient condition of existence for this pruning strategy, which can be applied to any measure.