Model-based Clustering: In this approach, clusters are represented by parametric distributions, and the data is modeled as a mixture of the specified distributions. For example, a set of clusters could be represented by different normal distributions, where each has a different mean and variance. This is referred to as a Gaussian Mixture Model, which is a powerful generative approach that can be used in clustering.