mltool-0.1.0.2: Machine Learning Toolbox

Copyright(c) Alexander Ignatyev 2017
LicenseBSD-3
Stabilityexperimental
PortabilityPOSIX
Safe HaskellNone
LanguageHaskell2010

MachineLearning.Clustering

Contents

Description

Cluster Analysis a.k.a. Clustering - grouping data into coherent subsets.

Synopsis

Documentation

type Cluster = Vector Vector Source #

Cluster type (list of samples associtaed with the cluster).

kmeans Source #

Arguments

:: RandomGen g 
=> Int

number of K-Means Algorithm runs (N);

-> Matrix

data to cluster;

-> Int

desired number of clusters (K);

-> Rand g (Vector Cluster)

list of clusters inside Random Monad.

Clusters data using K-Means Algorithm inside Random Monad. Runs K-Means algorithm N times, returns the clustering with smaller cost.

Exported for testing purposes only.

kmeansIterM Source #

Arguments

:: RandomGen g 
=> Vector Vector

list of samples;

-> Int

number of clusters (K);

-> Int

iteration number;

-> Rand g (Vector Cluster, [R])

(list of clusters, cost values) inside Random Monad.

Run K-Means algorithm once inside Random Monad.