CARs-Lands: an Associative Classifier for Large-scale Datasets

•This paper presents an efficient distributed associative classifier (CARs-Lands).•In CARs-Lands, local association rules lead to more accurate prediction, because each test instance is classified by the association rules of their nearest neighbors in the training datasets.•The proposed approach is evaluated in terms of accuracy on six real-world large-scale datasets against four other recent and well-known methods.•The experiment results show that the proposed classification method has a high prediction accuracy and is highly competitive when compared to other classification methods.