Probabilistic Clustering

Probabilistic clustering assumes that the data is generated from a mixture of probability distributions. Instead of assigning each data point to a single cluster, it provides the probability of a data point belonging to each cluster. A common example is the Gaussian Mixture Model (GMM), where it's assumed that the data points are generated from a mixture of Gaussian distributions. Scikit-learn provides an implementation of GMM that can be used for probabilistic clustering.

Visit the following resources to learn more:

• @article@Gaussian mixture models
• @article@Gaussian Mixture Model Explained