Title: PMCR-Miner: parallel maximal confident association rules miner algorithm for microarray data set

Authors: Wael Zakaria; Yasser Kotb; Fayed F.M. Ghaleb

Addresses: Computer Science Division, Department of Mathematics, Faculty of Science, Ain Shams University, Cairo, Egypt ' Information Systems Department, College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia; Computer Science Division, Department of Mathematics, Faculty of Science, Ain Shams University, Cairo, Egypt ' Computer Science Division, Department of Mathematics, Faculty of Science, Ain Shams University, Cairo, Egypt

Abstract: The MCR-Miner algorithm is aimed to mine all maximal high confident association rules form the microarray up/down-expressed genes data set. This paper introduces two new algorithms: IMCR-Miner and PMCR-Miner. The IMCR-Miner algorithm is an extension of the MCR-Miner algorithm with some improvements. These improvements implement a novel way to store the samples of each gene into a list of unsigned integers in order to benefit using the bitwise operations. In addition, the IMCR-Miner algorithm overcomes the drawbacks faced by the MCR-Miner algorithm by setting some restrictions to ignore repeated comparisons. The PMCR-Miner algorithm is a parallel version of the new proposed IMCR-Miner algorithm. The PMCR-Miner algorithm is based on shared-memory systems and task parallelism, where no time is needed in the process of sharing and combining data between processors. The experimental results on real microarray data sets show that the PMCR-Miner algorithm is more efficient and scalable than the counterparts.

Keywords: data mining; DNA microarrays; association rules mining; closed item sets; row enumeration; column enumeration; maximal high confident rules; bitwise operations; shared memory systems; task parallelism; gene expression; bioinformatics.

DOI: 10.1504/IJDMB.2015.072091

International Journal of Data Mining and Bioinformatics, 2015 Vol.13 No.3, pp.225 - 247

Received: 24 Jun 2014
Accepted: 14 Jan 2015
Published online: 30 Sep 2015 *

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