Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 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
Efficient search for association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Selecting the right objective measure for association analysis
Information Systems - Knowledge discovery and data mining (KDD 2002)
An experiment with association rules and classification: post-bagging and conviction
DS'05 Proceedings of the 8th international conference on Discovery Science
Mining classification rules without support: an anti-monotone property of Jaccard measure
DS'11 Proceedings of the 14th international conference on Discovery science
PAISI'10 Proceedings of the 2010 Pacific Asia conference on Intelligence and Security Informatics
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We study the predictive ability of some association rule measures typically used to assess descriptive interest. Such measures, namely conviction, lift and 茂戮驴2are compared with confidence, Laplace, mutual information, cosine, Jaccard and 茂戮驴-coefficient. As prediction models, we use sets of association rules. Classification is done by selecting the best rule, or by weighted voting. We performed an evaluation on 17 datasets with different characteristics and conclude that conviction is on average the best predictive measure to use in this setting. We also provide some meta-analysis insights for explaining the results.