COVID-19 Dataset Clustering based on K-Means and EM Algorithms
International Journal of Advanced Computer Science and Applications
; 14(3):924-934, 2023.
Article
in English
| Scopus | ID: covidwho-2292513
ABSTRACT
In this paper, a COVID-19 dataset is analyzed using a combination of K-Means and Expectation-Maximization (EM) algorithms to cluster the data. The purpose of this method is to gain insight into and interpret the various components of the data. The study focuses on tracking the evolution of confirmed, death, and recovered cases from March to October 2020, using a two-dimensional dataset approach. K-Means is used to group the data into three categories "Confirmed-Recovered”, "Confirmed-Death”, and "Recovered-Death”, and each category is modeled using a bivariate Gaussian density. The optimal value for k, which represents the number of groups, is determined using the Elbow method. The results indicate that the clusters generated by K-Means provide limited information, whereas the EM algorithm reveals the correlation between "Confirmed-Recovered”, "Confirmed-Death”, and "Recovered-Death”. The advantages of using the EM algorithm include stability in computation and improved clustering through the Gaussian Mixture Model (GMM). © 2023,International Journal of Advanced Computer Science and Applications. All Rights Reserved.
clustering; COVID-19; EM algorithm; GMM; k-means; Gaussian distribution; Image segmentation; K-means clustering; Maximum principle; Recovery; Bivariate gaussian; Clusterings; Dataset clustering; Expectations maximization algorithms; Gain insight; Gaussian density; Gaussian Mixture Model; Three categories; Two-dimensional
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
International Journal of Advanced Computer Science and Applications
Year:
2023
Document Type:
Article
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