Abstract
Associative classification integrates both association rule mining and classification tasks. Many studies show that Associative Classifiers give better accuracy than other traditional classifiers. Traditional classification techniques such as decision trees and RIPPER use heuristic search methods to perform classification. Associative classification system is more robust and makes predictions based on entire dataset. In this paper, we propose some criteria for ranking the association rules. This improves the overall accuracy of the classifier. Our preliminary results with some UCI ML datasets are very encouraging.
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