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
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Associative classification which uses association rules for classification has achieved high accuracy in comparison with other classification approaches. However, the confidence measure which is conventionally used for selecting association rules for classification may not conform to the prediction accuracy of the rules. In this paper, we propose a measure called prediction confidence to measure the prediction accuracy of association rules. In addition, a probabilistic-based approach for estimating prediction confidence of association rules is given and its performance is evaluated. The use of prediction confidence helps improve the performance of associative classifiers.