Gaussian mixture models provide a framework for clustering data by assuming it can be divided into distinct groups. The process involves inferring parameters like cluster centers and variances from observed data points, while also grappling with the challenge of determining the number of clusters. Traditional methods often require prior knowledge of the number of clusters, which is not always feasible in practice.
