Abstract
The Coronavirus disease 2019 SARS-CoV-2 is a disease which causes fear to human lives that has taken thousands and hundreds of lives globally. The pandemic which has resulted in a global health emergency is currently a much sought-after research topic. The frequently mutating virus which has originated from Chiroptera and subsequently got transmitted to other mammals including humans. However, at the genomic level, it is yet to be unraveled what makes humans more prone to getting infected by the coronaviruses. Here, we have implemented a Machine Learning model known as K-means Clustering that uses the combination of different features to determine the risk of infection. In this research paper, the K-means clustering method is used since it is a good performer for Clustering analysis. The algorithm can group the sequences of the dataset into five clusters based on the Elbow plot and co-linearity of co-efficient. Using dimensional reduction technique PCA is used with a 3D visualization and a heat map to showcase the correlation efficiency between the mutated and original sequence considered.
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Rath, S.L., Sinha, C., Kasturi, S.L.N.P., Mohapatra, S., Jain, K. (2022). An Unsupervised Clustering Algorithm to Cluster the New SARS-CoV-2 Virus Mutation. In: Saini, H.S., Sayal, R., Govardhan, A., Buyya, R. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 385. Springer, Singapore. https://doi.org/10.1007/978-981-16-8987-1_19
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DOI: https://doi.org/10.1007/978-981-16-8987-1_19
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