Gaussian Mixture Model vs k-means — what's the difference?
mediumAnswer
- k-means gives hard assignments and assumes spherical, equal-variance clusters.
- A GMM models each cluster as a multivariate Gaussian and returns soft (probabilistic) memberships; it handles ellipsoidal clusters and different covariances.
- GMM is fit with the EM algorithm.
- Use GMM when clusters overlap, have different shapes, or when you need probabilities.
Check yourself — multiple choice
- GMM gives hard assignments only
- GMM handles ellipsoidal clusters and soft assignments
- k-means models Gaussian densities explicitly
- GMM is trained with gradient descent only
GMM = soft, ellipsoidal clusters, trained with EM.
#clustering#density
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