An Inception towards Better Big Data Clustering Technique
P S. Md. Mujeeb1, R. Praveen Sam2, K.Madhavi3
1P S. Md. Mujeeb, Research Scholar, JNTUA, Ananthapuramu (Andhra Pradesh), India.
2Dr. R. Praveen Sam, Professor, Department of CSE, GPREC, Kurnool (Andhra Pradesh), India.
3Dr. K. Madhavi, Associate Professor, Department of CSE, JNTUACEA, Ananthapuramu (Andhra Pradesh), India.
Manuscript received on 22 April 2019 | Revised Manuscript received on 01 May 2019 | Manuscript Published on 05 May 2019 | PP: 188-191 | Volume-8 Issue-2S2, May 2019 | Retrieval Number: B10400182S219/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The speedy emanating technology during past few year in the area of information technology is “Big Data”. Clustering is one of the crucial task in broad range of domains handling enormous data. This survey presents the various clustering approaches adopted for the effective big data clustering. Thus, this review article provides the review of 15 research papers suggesting various methods adopted for the effective big data clustering, like K-means clustering, Variant of K-means clustering, Fuzzy C-means clustering, Possibilistic C-means clustering, Collaborative filtering and Optimization based clustering. Moreover, an elaborative analysis is done by concerning the implementation tools used, datasets utilized and the adopted framework for clustering of big data. Subsequently an effective scheme must be developed to surpass present techniques for exceptional management of big data. Eventually the research issues and gaps of various big data clustering techniques are presented for benefiting the researchers for inception towards better big data clustering.
Keywords: Big Data, Mapreduce, Clustering, K-Mean, C-Mean.
Scope of the Article: Clustering