AI.Clustering.KMeans

Kmeans clustering

Documentation

data KMeans

Results from running kmeans

Constructors

KMeans

Fields

_clusters :: Vector Int

A vector of integers (0 ~ k-1) indicating the cluster to which each point is allocated.

_centers :: Matrix Double

A matrix of cluster centres.

Instances

Show KMeans

kmeans

Arguments

:: PrimMonad m

=> Gen (PrimState m)

-> Initialization

-> Int

number of clusters

-> Matrix Double

each row represents a point

-> m KMeans

Lloyd's algorithm, also known as K-means algorithm

kmeansWith

Arguments

:: Matrix Double

initial set of k centroids

-> Matrix Double

each row represents a point

-> KMeans

Lloyd's algorithm, also known as K-means algorithm

Initialization methods

data Initialization

Different initialization methods

Constructors

Forgy

The Forgy method randomly chooses k unique observations from the data set and uses these as the initial means

KMeansPP

K-means++ algorithm, not implemented.

decode :: KMeans -> [a] -> [[a]]

Assign data to clusters based on KMeans result