The objective of our study is to find meaningful groups in the data of ischemic stroke patients using unsupervised clustering. The data are modeled using Gaussian mixture models with a variety of covariance structures. Cluster parameters in each of these models are estimated by maximum likelihood via the Expectation-Maximization algorithm. The best models are then selected by relying on information-theoretic criteria. It is observed that the stroke patients can be grouped into a small number of medically relevant clusters that are defined primarily by the presence of diabetes and atrial fibrillation. Characteristics of the clusters found are discussed, using statistical comparisons and data visualization.