AI.Clustering.KMeans

Kmeans clustering

Documentation

kmeans

Arguments

:: (Vector v Double, Vector v Int, Eq (v Int), Eq (v Double), PrimMonad m)

=> Gen (PrimState m)

-> Int

number of clusters

-> Matrix v Double

each row represents a point

-> m (v Int, Matrix v Double)

membership vector

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

kmeansWith

Arguments

:: (Vector v Double, Vector v Int, Eq (v Int))

=> Matrix v Double

initial set of k centroids

-> Matrix v Double

each row represents a point

-> (v Int, Matrix v Double)

membership vector and centroids

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

forgyMethod :: (PrimMonad m, Vector v a, Eq (v a)) => Gen (PrimState m) -> Int -> Matrix v a -> m (Matrix v a)

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