# riem.kmeans: K-Means Clustering In Riemann: Learning with Data on Riemannian Manifolds

 riem.kmeans R Documentation

## K-Means Clustering

### Description

Given N observations X_1, X_2, …, X_N \in \mathcal{M}, perform k-means clustering by minimizing within-cluster sum of squares (WCSS). Since the problem is NP-hard and sensitive to the initialization, we provide an option with multiple starts and return the best result with respect to WCSS.

### Usage

riem.kmeans(riemobj, k = 2, geometry = c("intrinsic", "extrinsic"), ...)


### Arguments

 riemobj a S3 "riemdata" class for N manifold-valued data. k the number of clusters. geometry (case-insensitive) name of geometry; either geodesic ("intrinsic") or embedded ("extrinsic") geometry. ... extra parameters including algorithm(case-insensitive) name of an algorithm; "MacQueen" (default), or "Lloyd". init(case-insensitive) name of an initialization scheme; "plus" for k-means++ (default), or "random". maxitermaximum number of iterations to be run (default:50). nstartthe number of random starts (default: 5).

### Value

a named list containing

cluster

a length-N vector of class labels (from 1:k).

means

a 3d array where each slice along 3rd dimension is a matrix representation of class mean.

score

within-cluster sum of squares (WCSS).

### References

\insertRef

lloyd_least_1982Riemann

\insertRef

macqueen_methods_1967Riemann

riem.kmeanspp

### Examples

#-------------------------------------------------------------------
#          Example on Sphere : a dataset with three types
#
# class 1 : 10 perturbed data points near (1,0,0) on S^2 in R^3
# class 2 : 10 perturbed data points near (0,1,0) on S^2 in R^3
# class 3 : 10 perturbed data points near (0,0,1) on S^2 in R^3
#-------------------------------------------------------------------
## GENERATE DATA
mydata = list()
for (i in 1:10){
tgt = c(1, stats::rnorm(2, sd=0.1))
mydata[[i]] = tgt/sqrt(sum(tgt^2))
}
for (i in 11:20){
tgt = c(rnorm(1,sd=0.1),1,rnorm(1,sd=0.1))
mydata[[i]] = tgt/sqrt(sum(tgt^2))
}
for (i in 21:30){
tgt = c(stats::rnorm(2, sd=0.1), 1)
mydata[[i]] = tgt/sqrt(sum(tgt^2))
}
myriem = wrap.sphere(mydata)
mylabs = rep(c(1,2,3), each=10)

## K-MEANS WITH K=2,3,4
clust2 = riem.kmeans(myriem, k=2)
clust3 = riem.kmeans(myriem, k=3)
clust4 = riem.kmeans(myriem, k=4)

## MDS FOR VISUALIZATION
mds2d = riem.mds(myriem, ndim=2)$embed ## VISUALIZE opar <- par(no.readonly=TRUE) par(mfrow=c(2,2), pty="s") plot(mds2d, pch=19, main="true label", col=mylabs) plot(mds2d, pch=19, main="K=2", col=clust2$cluster)
plot(mds2d, pch=19, main="K=3", col=clust3$cluster) plot(mds2d, pch=19, main="K=4", col=clust4$cluster)
par(opar)



Riemann documentation built on March 18, 2022, 7:55 p.m.