An Efficient Parallel and Distributed Algorithm on Top of MapReduce
G. Karuna1, I. Rama Krishna2, G. Venkata Rami Reddy3
1G. Karuna, Computer Science and Engineering, GRIET, Hyderabad, India.
2I. Rama Krishna, School of Information Technology, JNTUH, Hyderabad, India.
3G. Venkata Rami Reddy, School of Information Technology, JNTUH, Hyderabad, India.
Manuscript received on 03 July 2019 | Revised Manuscript received on 08 July 2019 | Manuscript published on 30 August 2019 | PP: 908-911 | Volume-8 Issue-10, August 2019 | Retrieval Number: J90750881019/2019©BEIESP | DOI: 10.35940/ijitee.J9075.0881019
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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 undertaking of subspace bunching is for discover concealed groups present in various subspaces inside of dataset. Lately, through the accumulate development of information extent as well as information measurements, conventional subspace grouping calculations convert wasteful just as ineffectual whereas extricating learning in the huge information condition, bringing about a rising need to structure productive parallel circulated subspace bunching calculations to deal with huge multi- dimensional information by an adequate calculus expense. This article provides MapReduce-dependent calculation of a parallel mafia subspace bunching. The calculation exploits MapReduce’s information apportioning in addition undertaking parallelism and accomplishes decent tradeoff amongst the expense for plate gets to besides correspondence fare. The exploratory results indicate near immediate accelerations and demonstrate the elevated adaptability and incredible opportunities for implementation of the suggested calculation.
Keywords: MapReduce, Parallelism, Subspace bunching
Scope of the Article: Parallel Computing