Gaussian Mixture Models
Gaussian Mixture Models (GMMs) are a probabilistic model used for clustering and density estimation, representing data as a mixture of multiple Gaussian distributions. They are an extension of k-means clustering that incorporates uncertainty and soft assignments, allowing each data point to belong to multiple clusters with varying probabilities. GMMs are widely applied in unsupervised learning tasks such as image segmentation, anomaly detection, and speech recognition.
Developers should learn GMMs when working on unsupervised learning problems where data exhibits complex, overlapping clusters, as they provide a flexible way to model multimodal distributions. They are particularly useful in scenarios requiring probabilistic interpretations, such as in Bayesian inference or when dealing with incomplete data using the Expectation-Maximization algorithm. For example, in customer segmentation or bioinformatics, GMMs can identify subgroups with nuanced patterns that hard clustering methods might miss.