Module clustering 

Clustering & mixture models (ISL ch 12, PRML ch 9).

• kmeans — Lloyd’s algorithm with k-means++ seeding.
• gmm — diagonal-covariance Gaussian mixture via EM (seeded by k-means).

Hierarchical/agglomerative clustering lands here next (build order in stats_plan.md).

Re-exports

pub use gmm::fit as fit_gmm;

pub use gmm::GmmModel;

pub use hierarchical::Hierarchical;

pub use hierarchical::Linkage;

pub use kmeans::fit as fit_kmeans;

pub use kmeans::KMeansModel;

Modules

gmm

Gaussian Mixture Models via EM (PRML ch 9.2, ISL ch 12) — diagonal-covariance mixture, the standard robust GMM. Means are seeded by k-means (reusing super::kmeans); the EM loop alternates responsibilities (E) and weighted moment updates (M) and is guaranteed to increase the log-likelihood each step.

hierarchical

Agglomerative hierarchical clustering (ISL ch 12.4.2) — bottom-up merging of the two closest clusters under a linkage rule, producing a dendrogram that can be cut into any number of clusters. Kernel-class AllPairs (the cluster distances).

kmeans

k-means clustering (ISL ch 12.4, PRML ch 9.1) — Lloyd’s algorithm with k-means++ seeding, over a row-major feature matrix.