package prbnmcn-clustering

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in-package search v0.2.0

prbnmcn-clustering
- Clustering
  - Agglomerative
    
    Element_set
    
    Make
    
    S
  - Helpers
  - Intf
    
    Metric
  - K_means
    
    Element
    
    Make
    
    E
  - K_medoids
    
    Make
    
    E
  - MultiStart

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K-means functor.

module type Element = sig ... end

type init =

| Forgy
(*
Forgy selects k elements at random (without replacement) as initial centroids.
*)
| RandomPartition
(*
Assigns each point to a random cluster, and computes the corresponding centroid. Note that these centroids do not necessarily belong to the dataset, which might cause robustness issues.
*)
| KmeansPP
(*
KmeansPP selects initial centroids iteratively with probabilities proportional to their squared distance to the previously selected centroids. This intuitively allows to spread them well.
*)

K-means is rather sensitive to the initial choice of centroids. This implementation provides several initialization algorithms, the standard one being Kmeans++ (KmeansPP)

type termination =

| Num_iter of int
| Threshold of float
| Min of constraints

Termination of the algorithm can be either specified as: 1) an /exact/ number of iterations Num_iter, 2) a Threshold giving the biggest delta-cost decrease under which we stop iterating, or 3) as the minimum of the above to, i.e. stop iterating when the cost-decrease is under threshold or when we reach max_iter.

and constraints = {

max_iter : int;
threshold : float;

}

exception KmeansError of string

Exception thrown by k_means in case something goes awry.

module Make (E : Element) : sig ... end