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

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.

Poor and rich dolphin optimization algorithm with modified deep fuzzy clustering for COVID‐19 patient analysis

“…The computational complexity of this model was high. Zubair et al 14 developed an effective K‐means clustering technique that discovered the initial centroids in an effective manner. The technique helped to discover the quality of health care using COVID‐19 databases.…”

“…In the classical K‐means clustering technique, the initial centroids are chosen in a random manner. Hence, discovering the initial coordinate points of centroids is considered as an imperative part in clustering 14 …”

“…Moreover, the small databases are utilized for simulation outcomes, which cannot offer insight on huge dataset. No time of execution was provided for the devised model 14 …”

“…Hence, discovering the initial coordinate points of centroids is considered as an imperative part in clustering. 14 …”

Grouping of the Second Wave of Covid-19 Infection Areas in East Java Province Using K-Means Clustering

“…This method is also quite effective and efficient in classifying Covid-19 cases, in a study conducted by Zubair et al, an analysis was carried out on how efficient this method was, and it was proven to provide good results and a high level of accuracy [19]. In addition, K-Means Clustering can also be used to determine the source of the spread of where this virus came from, so using this method will provide more accurate results and can also provide a more complete analysis.…”