vcgKmeans: fast Kmean clustering for 1D, 2D and 3D data

In Rvcg: Manipulations of Triangular Meshes Based on the 'VCGLIB' API

fast Kmean clustering for 1D, 2D and 3D data

Description

fast Kmean clustering for 1D, 2D and 3D data

Usage

vcgKmeans(x, k = 10, iter.max = 10, getClosest = FALSE, threads = 0)

Arguments

x	matrix containing coordinates or mesh3d
k	number of clusters
iter.max	maximum number of iterations
getClosest	logical: if TRUE the indices of the points closest to the k-centers are sought.
threads	integer: number of threads to use

Value

returns a list containing

centers	cluster center
class	vector with cluster association for each coordinate

If getClosest=TRUE

selected

vector with indices of points closest to the centers

See Also

vcgSample

Examples

require(Rvcg);require(rgl)
data(humface)
set.seed(42)
clust <- vcgKmeans(humface,k=1000,threads=1)

Rvcg documentation built on Sept. 30, 2024, 9:39 a.m.