Parallel diffrential evolution clustering algorithm based on MapReduce

Cancer research is a challenging and competitive field. The study of gene expression data has enabled the discovery of unknown types of cancer using unsupervised learning. However, genomic sequence data are increasing in an exponential manner. Indeed, since 2011 the global annual sequencing capacity is estimated to be quadrillions of bases and counting. To cope with this issue, we propose, in this paper, the implementation of differential evolution clustering algorithm using MapReduce methodology in order to deal with big data. The proposed algorithm consists in three consecutive levels. Experiments were conducted on 18 real gene expression data sets. The obtained results have shown that our approach is effective and competes with existing algorithms.

I. Introduction

Modern biology presents new challenges in terms of data management, query and analysis. In cancer research Microarray development has enabled researchers to obtain gene expression data from the most diverse and complex biological phenomena. Obtaining the data is only the first step towards the discovery of the unknown types of cancer. To transform gene expression data into knowledge, clustering is the frequently used method; it aims at extracting hidden relationships between genes by grouping them into several clusters, within each cluster a set of genes share a common features.