Associative classification models are based on two different data mining paradigms, namely pattern classification and association rule mining. These models are very popular for building highly accurate classifiers and have been employed in a number of real world applications. During the last years, several studies and different algorithms have been proposed to integrate associa- tive classification models with the fuzzy set theory, leading to the so-called fuzzy associative classifiers. In this paper, we propose a novel efficient fuzzy associative classification approach based on a fuzzy frequent pattern mining algorithm. Fuzzy items are generated by discretizing the input variables and defining strong fuzzy partitions on the intervals resulting from these discretizations. Then, fuzzy associa- tive classification rules are mined by employing a fuzzy extension of the FP-Growth algorithm, one of the most efficient frequent pattern mining algorithms. Finally, a set of highly accurate classification rules is generated after a pruning stage. We tested our approach on seventeen real-world datasets and compared the achieved results with the ones obtained by using both a non-fuzzy associative classifier, namely CMAR, and two recent state-of-the-art classifiers, namely FARC-HD and D-MOFARC, based on fuzzy association rules. Using non-parametric statistical tests, we show that our approach outperforms CMAR and achieves accuracies similar to FARC-HD and D-MOFARC
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