Bayesian Clustering

Bayesian clustering is a statistical method used to group similar items or data points based on their characteristics. It uses Bayes' theorem, which combines prior knowledge and observed data to make informed predictions. In this context, it assumes that data can be expressed as a mixture of different clusters, each with its own properties. By analyzing the data, Bayesian clustering updates its beliefs about which items belong together, continuously refining the clusters as new information is introduced. This approach is valuable for discovering patterns in complex datasets, providing insights in fields like marketing, biology, and social sciences.