Gaussian Mixture Models: Soft Clustering with the EM Algorithm
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K-Means is the clustering algorithm everyone learns first. But it makes two strong assumptions: every point belongs fully to exactly one cluster (hard assignment), and clusters are round blobs. Real data breaks both. Gaussian Mixture Models (GMMs) relax them — elliptical clusters, and soft probabilistic membership — and they're fit with the elegant EM algorithm . Data as a mixture of Gaussians A GMM assumes your data was generated by several Gaussian "bell curves" mixed together. Each component…
1Key Takeaways
- K-Means is the clustering algorithm everyone learns first.
- But it makes two strong assumptions: every point belongs fully to exactly one cluster (hard assignment), and clusters are round blobs.
- Gaussian Mixture Models (GMMs) relax them — elliptical clusters, and soft probabilistic membership — and they're fit with the elegant EM algorithm .
- Data as a mixture of Gaussians A GMM assumes your data was generated by several Gaussian "bell curves" mixed together.
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that k-Means is the clustering algorithm everyone learns first.
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