C4.5: programs for machine learning
C4.5: programs for machine learning
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
ECML '93 Proceedings of the European Conference on Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
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Associative classification is a well-known technique which uses association rules to predict the class label for new data object. This model has been recently reported to achieve higher accuracy than traditional classification approaches like C4.5. In this paper, we propose a novel associative classification algorithm based on correlation analysis, ACBCA, which aims at extracting the k-best strong correlated positive and negative association rules directly from training set for classification, avoiding to appoint complex support and confidence threshold. ACBCA integrates the advantages of the previously proposed effective strategies as well as the new strategies presented in this paper. An extensive performance study reveals that the improvement of ACBCA outperform other associative classification approaches on accuracy.