An Efficient K-means Clustering Algorithm for Analysing COVID-19

TL;DR

This paper introduces an improved K-means clustering algorithm that efficiently initializes centroids, enabling faster analysis of COVID-19 data to classify countries by healthcare quality.

Contribution

The paper presents a novel method for initializing centroids in K-means, reducing iterations and execution time in COVID-19 data clustering.

Findings

Reduces the number of iterations needed for convergence.

Decreases execution time compared to traditional K-means.

Effectively clusters countries by healthcare quality using COVID-19 data.

Abstract

COVID-19 hits the world like a storm by arising pandemic situations for most of the countries around the world. The whole world is trying to overcome this pandemic situation. A better health care quality may help a country to tackle the pandemic. Making clusters of countries with similar types of health care quality provides an insight into the quality of health care in different countries. In the area of machine learning and data science, the K-means clustering algorithm is typically used to create clusters based on similarity. In this paper, we propose an efficient K-means clustering method that determines the initial centroids of the clusters efficiently. Based on this proposed method, we have determined health care quality clusters of countries utilizing the COVID-19 datasets. Experimental results show that our proposed method reduces the number of iterations and execution time to analyze COVID-19 while comparing with the traditional k-means clustering algorithm.