gmm
GMM - Gaussian Mixture Models
Description: GMM assumes data is generated from a mixture of several Gaussian distributions, each representing a cluster.
Key Points:
- Can model clusters with different shapes and sizes.
- Uses probabilistic soft assignments of points to clusters.
- Sensitive to initialization and can converge to local optima.
Applications: Image segmentation, anomaly detection, finance