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

Description Usage Arguments Value See Also Examples

View source: R/vcgKmeans.r

Description

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

Usage

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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

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

zarquon42b/Rvcg documentation built on Jan. 17, 2019, 9:10 a.m.