CARs-Lands: An associative classifier for large-scale datasets

Highlights：

• 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.