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 centers.

Instances

Show KMeans

kmeans :: (PrimMonad m, Matrix mat Vector Double) => Gen (PrimState m) -> Method -> Int -> mat Vector Double -> m KMeans

Perform K-means clustering

kmeansBy

Arguments

:: (PrimMonad m, Vector v a)

=> Gen (PrimState m)

-> Method

-> Int

number of clusters

-> v a

data stores in rows

-> (a -> Vector Double)

-> m KMeans

K-means algorithm

kmeansWith

Arguments

:: Vector v a

=> Matrix Double

initial set of k centroids

-> v a

each row represents a point

-> (a -> Vector Double)

-> KMeans

K-means algorithm

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.

Useful functions

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

Assign data to clusters based on KMeans result

withinSS :: KMeans -> Matrix Double -> [Double]

Compute within-cluster sum of squares

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.