A Gaussian mixture model (GMM) is a generative model that assumes the data has been generated by a mix of various multivariate normal distributions. The algorithm aims to estimate the mean and covariance matrices of these distributions.

It generalizes the k-Means algorithm: it adds covariance among features so that clusters can be ellipsoids rather than spheres, while the centroids are represented by the means of each distribution. The GMM algorithm performs soft assignments because each point has the probability to be a member of any cluster.