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DOI: 10.14569/IJACSA.2023.01403105
PDF

COVID-19 Dataset Clustering based on K-Means and EM Algorithms

Author 1: Youssef Boutazart
Author 2: Hassan Satori
Author 3: Anselme R. Affane M
Author 4: Mohamed Hamidi
Author 5: Khaled Satori

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 3, 2023.

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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).

Keywords: COVID-19; clustering; k-means; EM algorithm; GMM

Youssef Boutazart, Hassan Satori, Anselme R. Affane M, Mohamed Hamidi and Khaled Satori, “COVID-19 Dataset Clustering based on K-Means and EM Algorithms” International Journal of Advanced Computer Science and Applications(IJACSA), 14(3), 2023. http://dx.doi.org/10.14569/IJACSA.2023.01403105

@article{Boutazart2023,
title = {COVID-19 Dataset Clustering based on K-Means and EM Algorithms},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01403105},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01403105},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {3},
author = {Youssef Boutazart and Hassan Satori and Anselme R. Affane M and Mohamed Hamidi and Khaled Satori}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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