Gaussian Mixture Models (GMM) are a popular unsupervised learning algorithm used for clustering and density estimation. They assume that the data points are generated from a mixture of several Gaussian distributions, each with its own mean and variance. This approach is more robust than simpler methods like K-Means clustering, as it incorporates information about the covariance structure of the data, allowing for more complex cluster shapes.

GMMs utilize the Expectation-Maximization (EM) algorithm for fitting the model to the data. The EM algorithm iteratively estimates the parameters of the Gaussian components and assigns probabilities to each data point, indicating the likelihood of belonging to each component. This process continues until convergence to a local optimum is achieved. Additionally, GMMs can be implemented using libraries like Scikit-learn, which provides tools for model selection and fitting ³.

In summary, GMMs are versatile and powerful for clustering tasks, especially when the underlying data distribution is not uniform or when the number of clusters is not known a priori.