Efficient strategies for parallel mining class association rules

•Three strategies for parallel mining CARs on the multi-core processor architecture.•Unlike other methods, the proposed method eliminates the synchronization among nodes.•Data replication and data transfer among processing units are also avoided.•The proposed algorithm is proven to be more efficient than existing ones in both theory and experiment.

Mining class association rules (CARs) is an essential, but time-intensive task in Associative Classification (AC). A number of algorithms have been proposed to speed up the mining process. However, sequential algorithms are not efficient for mining CARs in large datasets while existing parallel algorithms require communication and collaboration among computing nodes which introduces the high cost of synchronization. This paper addresses these drawbacks by proposing three efficient approaches for mining CARs in large datasets relying on parallel computing. To date, this is the first study which tries to implement an algorithm for parallel mining CARs on a computer with the multi-core processor architecture. The proposed parallel algorithm is theoretically proven to be faster than existing parallel algorithms. The experimental results also show that our proposed parallel algorithm outperforms a recent sequential algorithm in mining time.