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Published: 2021-02-22

Page: 32-38


Department of Botany, University of Kashmir, Srinagar-190006, Jammu and Kashmir, India.


Department of Botany, University of Kashmir, Srinagar-190006, Jammu and Kashmir, India. 2Department of Technical Education, IET, Lucknow, India.


Department of Botany, University of Kashmir, Srinagar-190006, Jammu and Kashmir, India.


Department of Technical Education, IET, Lucknow, India.

*Author to whom correspondence should be addressed.


Objectives: In December 2019, in Wuhan, China, a novel coronavirus disease (COVID-19), a highly infectious disease, was first described. The disease has spread to 210 countries and territories across the world and more than two million people have been infected (confirmed). In India, the disease was first detected on 30 January 2020 in Kerala in a student who returned from Wuhan. The disease has been continuously spreading all the state of India. The main objective of this study was to identify and classify affected districts into real clusters on the basis of observations of similarities within a cluster and dissimilarities among different clusters so that government policies, decisions, medical facilities (ventilators, testing kits, masks, treatment etc.), etc. could be improved for reducing the number of infected and deceased persons and hence cured cased could be increased.

Materials and Methods: We concentrated on the COVID-19 affected states and UTs of India in the report. To fulfill the task, we applied cluster analysis, one of the data mining techniques. The study of variations among various clusters for each of the variables was performed using box plots. We used PAST software for getting for getting a scatter plot for each of the variables.

Results: Results obtained from the clustering analysis and box plot methods for each of the variables. For confirmed cases, cluster I corresponded to the states AP, AR, AS, BR, CG, GA, GJ, HR, HP, JH, KA, KL, MP, MH, MN, ML, MZ, NL, OR, PB, RJ, SK, TN, TG, TR, UP, UK, WB, AN, CH, DNDD, DL, JK, LA, LD, PY. For cured cases, cluster II and for death cases, cluster III corresponded to all the states and UTs of India.

Conclusions: The study showed that the state MH, AP, AR, DL and KL under cluster I have a high number of confirmed cases. The box plots and histogram shows variations among different clusters of the three cases. The trend in box plots and histograms showed a good percentage of cured cases in some of the states and UTs. It was observed that the states (MH, UP, KR, TN, DL and WB) under clusters III had severe conditions which need optimization of monitoring techniques which could help the government in making improvement government policies, actions, etc. to reduce the number of infected persons.


Keywords: Coronavirus disease-19, India, cluster analysis, box plot, data mining.

How to Cite

ZARGAR, S. A., ISLAM, T., REHMAN, I. U., & PANDEY, D. (2021). USE OF CLUSTER ANALYSIS TO MONITOR NOVEL CORONA VIRUS (COVID-19) INFECTIONS IN INDIA. Asian Journal of Advances in Medical Science, 3(1), 32–38. Retrieved from


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