COVID-19 PANDEMIC DATASETS BASED ON MACHINE LEARNING CLUSTERING ALGORITHMS: A REVIEW

Abstract

COVID-19, standing for Corona Virus Disease in 2019, was a major problem emerged in 2019 and 2020. This has led many intellectuals and scientists to think creatively of ways to eradicate its negative effects. Accordingly, many people have been referring to this pandemic on social media and other media outlets. Within this reference, people have given a lot of data and predictions. Computer intelligence and digital analysis is also one of the fields that has taken this into consideration using clustering algorithms. Clustering can be defined as an approach to place identical data in one community or cluster and separate unsimilar data in another group. There are many Clustering algorithms used for clustering COVID-19 Pandemic datasets. The aims of this paper are to give an overview of the clustering algorithms used in case of COVID-19 datasets, to show how these algorithms help to provide accuracy for clustering the COVID-19 Pandemic, and provide an explanation of the algorithms used for the purpose of either lessening or controlling COVID-19 that are discussed in different papers. Moreover, it details the datasets in terms of different variables like temperature, countries, media outlets including social media, and present the findings of these papers, and which clustering algorithms used and the accuracy of these algorithms. It is found out from the present overview that the clustering algorithm k-means is used widely in different types of the COVID-19 datasets with high accuracy.