R: Initiate centers for clustering algorithm

initiate_centers {doMIsaul}

R Documentation

Initiate centers for clustering algorithm

Initiate centers for clustering algorithm

initiate_centers(data, N = 1000, t = 1, k, algorithms = NULL, seeds.N = NULL)

`data`	Dataset that clustering will be applied on
`N`	Integer. Number clustering initialization (set of centers) to generate
`t`	Numeric between 0 and 1. weight coefficient between only random centers (t=1) and only centers from clustering (t=0).
`k`	Vector of size N containing the number of centers for each initialization.
`algorithms`	list of algorithm(s) (size `N * (1-t)` to generate centers if t!=1, given as characters. Possible values are "`km`" for 'K-means', "`kmed`" for 'K-medians', `"hclust.mean", "hclust.med"` for hierarchical clustering with mean or median position of the center.
`seeds.N`	(optional) vector of size N containing seeds for each initialization.

list of size N containing coordinates of centers for clustering initialization.

Cent.init <- initiate_centers(data = iris[, 1:4], N = 10,
                              k = sample(c(2:7), 10, replace = TRUE))

[Package doMIsaul version 1.0.1 Index]