Article ID Journal Published Year Pages File Type
387716 Expert Systems with Applications 2012 9 Pages PDF
Abstract

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.

► Advantages of specific rules with high confidence in classification based on CARs. ► Avoid ambiguity at the classification stage may increase the classification accuracy. ► A new coverage strategy, during the classification stage is proposed.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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