A MapReduce approach to diminish imbalance parameters for big deoxyribonucleic acid dataset.
BACKGROUND
In the age of information superhighway, big data play a significant role in information processing, extractions, retrieving and management. In computational biology, the continuous challenge is to manage the biological data. Data mining techniques are sometimes imperfect for new space and time requirements. Thus, it is critical to process massive amounts of data to retrieve knowledge. The existing software and automated tools to handle big data sets are not sufficient. As a result, an expandable mining technique that enfolds the large storage and processing capability of distributed or parallel processing platforms is essential.


METHODS
In this analysis, a contemporary distributed clustering methodology for imbalance data reduction using k-nearest neighbor (K-NN) classification approach has been introduced. The pivotal objective of this work is to illustrate real training data sets with reduced amount of elements or instances. These reduced amounts of data sets will ensure faster data classification and standard storage management with less sensitivity. However, general data reduction methods cannot manage very big data sets. To minimize these difficulties, a MapReduce-oriented framework is designed using various clusters of automated contents, comprising multiple algorithmic approaches.


RESULTS
To test the proposed approach, a real DNA (deoxyribonucleic acid) dataset that consists of 90 million pairs has been used. The proposed model reduces the imbalance data sets from large-scale data sets without loss of its accuracy.


CONCLUSIONS
The obtained results depict that MapReduce based K-NN classifier provided accurate results for big data of DNA.
Computer Science and Engineering, East West University, Dhaka, Bangladesh.
Sarwar
S
Kamal
Computer Science and Engineering, East West University, Dhaka, Bangladesh.
Shamim Hasnat
SH
Ripon
Techno India Institute of Technology, Kolkata, India.
Nilanjan
N
Dey
Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt. Electronic address: amirasashour@yahoo.com.
Amira S
AS
Ashour
School of Computing Science and Engineering, VIT University, Vellore, Tamil Nadu, India.
V
V
Santhi
D000465:Algorithms; D004247:DNA / Q000737:chemistry*; D066264:Datasets as Topic; D006801:Humans
D004247:DNA
Big data; Computational biology; DNA (deoxyribonucleic acid); Imbalance data; K-nearest neighbor; MapReduce