#data mining.

Objectives: A novel coronavirus illness (COVID-19), a highly infectious disease, was first characterised in December 2019 in Wuhan, China. More than two million individuals have been infected with the disease, which has spread to 210 nations and territories throughout the world (confirmed). The sickness was initially discovered in India on January 30, 2020, in Kerala, in a student who had returned from Wuhan. The disease has been spreading throughout India's states. The study's main goal was to identify and classify affected districts into real clusters based on similarities within a cluster and differences between clusters, so that government policies, decisions, and medical facilities (ventilators, testing kits, masks, treatment, etc.) could be improved in order to reduce the number of infected and deceased people.

Materials and Methods: In this paper, we focused on the COVID-19-affected states and union territories of India. We used cluster analysis, a data mining technique, to complete the work. Box plots were used to examine variances among various clusters for each of the variables. For each of the variables, we used the PAST software to create a scatter plot.

For each of the variables, the results of the clustering analysis and box plot approaches were obtained. Cluster I linked 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 verified cases. Cluster II corresponded to all Indian states and union territories for cured patients, while cluster III belonged to all Indian states and union territories for death cases.

Conclusions: The study found that the cluster I states of MH, AP, AR, DL, and KL have a high number of confirmed cases. The box plots and histogram demonstrate differences between the three cases' clusters. In various states and UTs, the trend in box plots and histograms revealed a high percentage of healed patients. It was discovered that the states in Cluster III (MH, UP, KR, TN, DL, and WB) had severe conditions that necessitated the optimization of monitoring techniques that could assist the government in improving government policies, actions, and so on in order to lower the number of infected people.