Open AccessCOVID 19 Data Clustering a nd Testing with K Means Mapper and Reducer
Author(s) -
Antenna Anusha,
AUTHOR_ID,
K. Kishore Raju,
AUTHOR_ID
Publication year - 2021
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b9654.1211221
Subject(s) - reducer , covid-19 , cluster analysis , volume (thermodynamics) , computer science , data mining , process (computing) , infectious disease (medical specialty) , artificial intelligence , medicine , virology , engineering , disease , operating system , pathology , physics , quantum mechanics , outbreak , civil engineering
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.