AI.Clustering.KMeans

Documentation

data KMeans a

Results from running kmeans

Constructors

KMeans

Fields

• membership :: 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 centers.

• clusters :: Maybe [[a]]

Instances

Show a => Show (KMeans a)

Methods

showsPrec :: Int -> KMeans a -> ShowS

show :: KMeans a -> String

showList :: [KMeans a] -> ShowS

data KMeansOpts

defaultKMeansOpts :: KMeansOpts

kmeans

Arguments

:: Int

The number of clusters

-> Matrix Double

Input data stored as rows in a matrix

-> KMeansOpts

-> KMeans (Vector Double)

Perform K-means clustering

kmeansBy

Arguments

:: Vector v a

=> Int

The number of clusters

-> v a

Input data

-> (a -> Vector Double)

-> KMeansOpts

-> KMeans a

Perform K-means clustering, using a feature extraction function

Initialization methods

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

References

Arthur, D. and Vassilvitskii, S. (2007). k-means++: the advantages of careful seeding. Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms. Society for Industrial and Applied Mathematics Philadelphia, PA, USA. pp. 1027–1035.