Title: An empirical study of the big data classification methodologies

Authors: S. Md. Mujeeb; R. Praveen Sam; K. Madhavi

Addresses: Department of CSE, Jawaharlal Nehru Technological University Anantapur (JNTUA), Ananthapuramu-515002, Andhra Pradesh, India ' Department of CSE, G.Pulla Reddy Engineering College (GPREC), Kurnool-518007, Andhra Pradesh, India ' Department of CSE, JNTUA College of Engineering Ananthapuramu (JNTUACEA) Ananthapuramu-515002, Andhra Pradesh, India

Abstract: The two hasty emanating technologies are big data and cloud computing. Cloud computing is a novel archetype for providing the computing environment in contrast the big data processing technology is convenient for most of the resource types. Now, a productive cloud-based methodology must be devised for the effective management of the big data. This survey presents the distinct cloud-based classification and clustering approaches adopted for the effective big data classification. This paper reviews 40 research papers in the field of big data classification methodologies, like fuzzy classifier, Bayesian model, support vector machine (SVM) classifier, K-means clustering, collaborative filtering based clustering and so on. Moreover, an elaborative analysis and discussion are made by concerning the employed methodology, evaluation metrics, accuracy range, adopted framework, datasets utilised and the implementation tool. Eventually, the research gaps and issues of various conventional cloud-based big data classification schemes are presented for extending the researchers towards a better contribution of significant big data management.

Keywords: big data; cloud computing; classification; clustering; fuzzy classifier; accuracy.

DOI: 10.1504/IJBRA.2020.108413

International Journal of Bioinformatics Research and Applications, 2020 Vol.16 No.2, pp.195 - 215

Received: 14 May 2018
Accepted: 25 Jun 2019
Published online: 13 Jul 2020 *

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