Community detection using unsupervised machine learning techniques on COVID-19 dataset

Chaudhary, Laxmi; Singh, Buddha

COVID-19 has been considered to be the most destructive pandemic ever happened in the history of mankind. The worldwide research community has put a tenacious effort to carry out research on the COVID-19 to analyse its impact on economic, medical and sociolgoical fields. They are trying to solve many crucial issues related to this disease and derive strategies to deal with this global pandemic. In this paper, we have analysed the trend, countries affected regionally and the variation of cases at the country level on COVID-19 dataset. We have used the Principal component analysis on the COVID-19 dataset variables to reduce the dimensionality and find the most significant variables. Further, we have unveiled the hidden community structure of countries by applying the unsupervised clustering approach, K-means. We have compared the results with the K-means method. The communities achieved after applying the PCA are more precise. The resulted communities can be beneficial to researchers, scientists, sociologists, different policy makers and managers of health sector.