initiate_centers: Initiate centers for clustering algorithm

In LilithF/doMIsaul: Do Multiple Imputation-Based Semi-Supervised and Unsupervised Learning

Description

Initiate centers for clustering algorithm

Usage

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

Arguments

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.

Value

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

Examples

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

LilithF/doMIsaul documentation built on Dec. 17, 2021, 12:08 a.m.