Parallel and distributed association rule mining in life science: A novel parallel algorithm to mine genomics data

Abstract

Association rule mining (ARM) is largely employed in several scientific areas and application domains, and many different algorithms for learning association rules from databases have been introduced. Despite the presence of many existing algorithms, there is still room for the introduction of novel approaches tailored for novel kinds of datasets. Because often the efficiency of such algorithms depends on the type of analyzed dataset. For instance, classical ARM algorithms present some drawbacks for biological datasets produced by microarray technologies in particular containing Single Nucleotide Polymorphisms (SNPs). In particular classical algorithms require large execution times also with small datasets. Therefore the possibility to improve the performance of such algorithms by leveraging parallel computing is a growing research area. The main contributions of this paper are: a comparison among different sequential, parallels and distributed ARM techniques, and the presentation of a novel ARM algorithm, named Balanced Parallel Association Rule Extractor from SNPs (BPARES), that employs parallel computing and a novel balancing strategy to improve response time. BPARES improves performance without loosing in accuracy as well as it handles more efficiently the available computational power and reduces the memory consumption.