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Classification based on specific rules and inexact coverage
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DOI：10.1016/j.eswa.2012.03.057.png)
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Abstract
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Association rule mining and classification are important tasks in data mining. Using association rules has proved to be a good approach for classification. In this paper, we propose an accurate classifier based on class association rules (CARs), called CAR-IC, which introduces a new pruning strategy for mining CARS, which allows building specific rules with high confidence. Moreover, we propose and prove three propositions that support the use of a confidence threshold for computing rules that avoids ambiguity at the classification stage. This paper also presents a new way for ordering the set of CARS based on rule size and confidence. Finally, we define a new coverage strategy, which reduces the number of non-covered unseen-transactions during the classification stage. Results over several datasets show that CAR-IC beats the best classifiers based on CARS reported in the literature. (C) 2012 Elsevier Ltd. All rights reserved.
Keywords:
Data mining
Supervised classification
Class association rules
Association rule mining
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