fastKmeans: fast kmeans clustering for 2D or 3D point clouds

In zarquon42b/Morpho: Calculations and Visualisations Related to Geometric Morphometrics

fast kmeans clustering for 2D or 3D point clouds

Description

fast kmeans clustering for 2D or 3D point clouds - with the primary purpose to get a spatially equally distributed samples

Usage

fastKmeans(x, k, iter.max = 10, project = TRUE, threads = 0)

Arguments

x	matrix containing coordinates or mesh3d
k	number of clusters
iter.max	maximum number of iterations
project	logical: if x is a triangular mesh, the centers will be projected onto the surface.
threads	integer number of threads to use

Value

returns a list containing

selected	coordinates closest to the final centers
centers	cluster center
class	vector with cluster association for each coordinate

Examples

require(Rvcg)
data(humface)
set.seed(42)
clust <- fastKmeans(humface,k=1000,threads=1)
## Not run: 
require(rgl)

## plot the cluster centers
spheres3d(clust$centers)

## now look at the vertices closest to the centers
wire3d(humface)
spheres3d(vert2points(humface)[clust$selected,],col=2)

## End(Not run)

zarquon42b/Morpho documentation built on Jan. 28, 2024, 2:11 p.m.