COVID 19 Data Clustering and Testing with K-Means Mapper and Reducer
A. Anusha1, K. Kishore Raju2
1A. Anusha*, Department of Information Technology, S.R.K.R. Engg College, China-Amiram, Bhimavaram (A.P).
2Dr. K. Kishore Raju, Assistant Professor, Department of Information Technology, S.R.K.R. Engg College, China-Amiram, Bhimavaram (A.P).
Manuscript received on December 08, 2021. | Revised Manuscript received on December 13, 2021. | Manuscript published on December 30, 2021. | PP: 23-25 | Volume-11, Issue-2, December 2021 | Retrieval Number: 100.1/ijitee.B96541211221 | DOI: 10.35940/ijitee.B9654.1211221
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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: Due to the emergence of a new infectious disease (COVID-19), the worldwide data volume has been quickly increasing at a very high rate during the last two years. Due its infectious, and importance, in this paper, K-Means clustering procedure is applied on COVID data in MapReduce based distributed computing environment. The proposed system is store, process and tests the large volume of COVID-19 data. Experimental results had been proved that this process is adaptable to COVID-19 data in the formation of trusted clusters.
Keywords: K-Means Clustering, MapReduce, Unsupervised Machine Learning and Covid.
Scope of the Article: Information Technology.