PARES (Parallel Association Rules Extractor from SNPs) is a novel parallel algorithm for the efficient extraction of Association Rules from omics datasets.
PARES is implemented as a multi-thread version of an optimized version of the Frequent Pattern Growth (FP-Growth) algorithm. Moreover, it includes a customized SNPs datasets preprocessing strategy based on a Fisher's Test Filter to discard the trivial transactions from the input dataset, reducing the search space from which to build many independent FP-Tree. The experimental results show that PARES has a good speedup and a high memory management efficiency, with respect to several association rule mining algorithms implemented in main off-the-shelf data mining platforms.