Dirichlet process mixture model

A Dirichlet process mixture model is a flexible statistical tool that groups data points into clusters without fixing the number of clusters in advance. It treats the overall data distribution as composed of many possible subgroups, allowing the number of clusters to grow or shrink as needed based on the data itself. This approach is useful for discovering natural groupings in complex data when the true number of clusters is unknown or uncertain. Essentially, it provides a way to model data with an adaptable number of underlying categories, balancing structure and flexibility in analysis.