  With the ongoing COVID-19 pandemic, understanding the characteristics of the
virus has become an important and challenging task in the scientific community.
While tests do exist for COVID-19, the goal of our research is to explore other
methods of identifying infected individuals. Our group applied unsupervised
clustering techniques to explore a dataset of lungscans of COVID-19 infected,
Viral Pneumonia infected, and healthy individuals. This is an important area to
explore as COVID-19 is a novel disease that is currently being studied in
detail. Our methodology explores the potential that unsupervised clustering
algorithms have to reveal important hidden differences between COVID-19 and
other respiratory illnesses. Our experiments use: Principal Component Analysis
(PCA), K-Means++ (KM++) and the recently developed Robust Continuous Clustering
algorithm (RCC). We evaluate the performance of KM++ and RCC in clustering
COVID-19 lung scans using the Adjusted Mutual Information (AMI) score.
