Clusterization of Different Vulnerable Countries for Immigrants Due to Covid-19 Using Mean Probabilistic Likelihood Score and Unsupervised Mining Algorithms

In many developing and poor countries, people often migrate to suitable countries to earn their livelihood and support their families. Due to the ongoing pecuniary disaster that ensued because of COVID-19, many immigrants are coming back to their ancestry from different migrant-friendly countries for several reasons. In this paper, a novel approach has been proposed to segregate these countries into five vulnerability labels based on probabilistic likelihood score (LHS) and unsupervised clustering algorithms (CA). A survey dataset of returnee people including various information has been collected and leveraged as attributes in this study. Depending on the dissemination of attributes, LHS has been generated using Bayes' Theorem for each vulnerable country and three unsupervised mining algorithms (KMeans++, Agglomerative and BIRCH) have been applied to the LHS for categorization. Output labels obtained from CA are then evaluated appropriating the average LHS. Multiple performance measurement metrics (Adjusted Rand Index, Mutual Information based Score) have been consolidated to get an incisive comparison of vulnerability labels resulting from CA and expected LHS. The highest value of 0.74 has been attained as Normalized Mutual Information based Score for BIRCH clustering accompanying ample results for the remaining algorithms. The result has shown that the combined application of probabilistic LHS and unsupervised CA can be a reliable method to identify the vulnerability of different countries generally chosen by migrant people.