Model-Based Clustering

Model-based clustering is a statistical approach to group similar items together based on their characteristics. It assumes that data points originate from underlying patterns or “models.” Each cluster represents a group that shares common traits, while the model explains how likely data points are to belong to each cluster. By using probabilities, this method can identify the best way to organize data, find natural groupings, and even reveal hidden structures in complex datasets. It's widely used in fields like marketing, biology, and social sciences to make sense of large amounts of information.