Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient mining of association rules using closed itemset lattices
Information Systems
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
Mining confident rules without support requirement
Proceedings of the tenth international conference on Information and knowledge management
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Classification Rule Learning with APRIORI-C
EPIA '01 Proceedings of the10th Portuguese Conference on Artificial Intelligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems, Logic Programming and Constraint Solving
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
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 Non-Redundant Association Rules
Data Mining and Knowledge Discovery
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)
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
A Unified View of Objective Interestingness Measures
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Comparing Rule Measures for Predictive Association Rules
ECML '07 Proceedings of the 18th European conference on Machine Learning
On Optimal Rule Mining: A Framework and a Necessary and Sufficient Condition of Antimonotonicity
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
All-Monotony: A Generalization of the All-Confidence Antimonotony
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
A study on interestingness measures for associative classifiers
Proceedings of the 2010 ACM Symposium on Applied Computing
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
Item set mining based on cover similarity
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
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We propose a general definition of anti-monotony, and study the anti-monotone property of the Jaccard measure for classification rules. The discovered property can be inserted in an Apriori-like algorithm and can prune the search space without any support constraint. Moreover, the algorithm is complete since, it outputs all interesting rules with respect to the measure of Jaccard. The proposed pruning strategy can then be used to efficiently find nuggets of knowledge.