clustering-0.3.1: High performance clustering algorithms

Copyright(c) 2015 Kai Zhang
LicenseMIT
Maintainerkai@kzhang.org
Stabilityexperimental
Portabilityportable
Safe HaskellNone
LanguageHaskell2010

AI.Clustering.KMeans.Types

Description

Synopsis

Documentation

data KMeansOpts #

Constructors

KMeansOpts 

Fields

data KMeans a #

Results from running kmeans

Constructors

KMeans 

Fields

Instances

Show a => Show (KMeans a) # 

Methods

showsPrec :: Int -> KMeans a -> ShowS #

show :: KMeans a -> String #

showList :: [KMeans a] -> ShowS #

data Method #

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.

Centers (Matrix Double)

Provide a set of k centroids