Gaussian Mixture Models (GMM): A Powerful Tool for Data Clustering

The Concept Behind GMM

At its core, GMM is based on the idea that complex datasets can often be represented as a combination of simpler, Gaussian-distributed clusters. Unlike hard clustering methods such as k-means, which assign each data point to a single cluster, GMM takes a probabilistic approach. Each data point is assigned a probability of belonging to each cluster, allowing for more nuanced groupings. This makes GMM particularly powerful in cases where clusters are not clearly separated and may overlap.

Flexibility and Adaptability

Applications in Data Science

Challenges and Considerations