Algorithm for clustering analysis of gene expression data using MapReduce framework

Bioinformatics is a fast growing field in data mining techniques to solve biological problems. Few decades' fast developments in genomic and other molecular research in information technologies have combined to produce an enormous amount of information relevant to molecular biology. The study o...

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Veröffentlicht in:2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE'16) S. 1 - 4
Hauptverfasser: Priya, P. Packia Amutha, Lawrance, R.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.01.2016
Schlagworte:
Algorithm design and analysis
Big data
Bioinformatics
Canopy Clustering Algorithm
Clustering
Clustering algorithms
Data mining
Gene expression
Gene expression data
MapReduce
Microarray
Partitioning algorithms
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Zusammenfassung:Bioinformatics is a fast growing field in data mining techniques to solve biological problems. Few decades' fast developments in genomic and other molecular research in information technologies have combined to produce an enormous amount of information relevant to molecular biology. The study of gene expression data investigation has developed in the few years from being purely data-centric to interrelate. The developments in gene expression based analysis methods are association, classification, clustering, and prediction studies. In recent times, industries have a rapid growth of data; a data analysis tool is required to satisfy the need for analyzing a huge volume of data. The MapReduce framework is designed to compute data demanding applications to support effective decision making. This document afford an outline of the MapReduce programming model, different methods to implement MapReduce models to process large-scale datasets and used to cluster the given gene dataset based on specified features.
DOI:10.1109/ICCTIDE.2016.7725376