Gaussian Mixtures assume the dataset is described by a finite numbers of weighted Gaussians

Expectation-Maximization is used to determined the weights and mean/covariances of the Gaussians

The initial centroids, controlled by the init_params parameter in SciKit Learn allow for initialization using kmeans, kmeans++, random, and random_from_data

Each iteration consists of an E-step and an M-step:

• E-step: compute the membership weights for all data points and centroids

• M-step: use the membership weights to calculate a new centroid

Mixture models generalize K-Means clustering by incorporating information about the covariance structure of the data and the centers of the latent Gaussians

The covariance adjusts the directions and lengths of the axes of the ellipsoidal density contours

SciKit Learn has four different parameter values for the covariance: diagonal, spherical, tied and full covariance