diffusionMap (version 1.2.0)

diffusionKmeans: Diffusion K-means

Description

Clusters a data set based on its diffusion coordinates.

Usage

diffusionKmeans(dmap, K, params = c(), Niter = 10, epsilon = 0.001)

Arguments

dmap

a '"dmap"' object, computed by diffuse()

K

number of clusters

params

optional parameters for each data point. Entry can be a vector of length n, or a matrix with n rows. If this argument is given, cluster centroid parameters are returned.

Niter

number of K-means iterations performed.

epsilon

stopping criterion for relative change in distortion for each K-means iteration

Value

The returned value is a list with components

part

final labelling of data from K-means. n-dimensional vector with integers between 1 and K

cent

K geometric centroids found by K-means

D

minimum of total distortion (loss function of K-means) found across K-means runs

DK

n by k matrix of squared (Euclidean) distances from each point to every centroid for the optimal K-means run

centparams

optional parameters for each centroid. Only returned if params is specified in the function call. Is a matrix with k rows.

Details

A '"dmap"' object computed by diffuse() is the input, so diffuse() must be performed first. Function is written this way so the K-means parameters may be varied without having to recompute the diffusion map coordinates in each run.

Diffusion K-means is a special form of spectral clustering. It is a unique algorithm because the eigenvectors of the symmetric Laplacian are weighted in such a way to guarantee that Euclidean distance in diffusion space will be approximately equal to the diffusion distance between objects. Clustering by Euclidean distance in diffusion space exploits this fact.

References

Lafon, S., & Lee, A., (2006), IEEE Trans. Pattern Anal. and Mach. Intel., 28, 1393

Richards, J. W., Freeman, P. E., Lee, A. B., and Schafer, C. M., (2009), ApJ, 691, 32

Richards, J. W., Freeman, P. E., Lee, A. B., Schafer, C. M., (2009), MNRAS, Volume 399, Issue 2, pp. 1044-1057

See Also

diffuse()

Examples

# NOT RUN {
library(scatterplot3d)

## example with annulus data set
data(annulus)
par(mfrow=c(2,1))
plot(annulus,main="Annulus Data",pch=20,cex=.7)
D = dist(annulus) # use Euclidean distance
dmap = diffuse(D,eps.val=0.05) # compute diffusion map
k=2  # number of clusters
dkmeans = diffusionKmeans(dmap, k)
plot(annulus,main="Colored by diffusion K-means clustering",pch=20,
   cex=.7,col=dkmeans$part)
table(dkmeans$part,c(rep(1,500),rep(2,500)))


## example with Chainlink data set
data(Chainlink)
lab.col = c(rep("red",500),rep("blue",500)); n=1000
scatterplot3d(Chainlink$C1,Chainlink$C2,Chainlink$C3,color=lab.col,
   main="Chainlink Data") # plot Chainlink data
D = dist(Chainlink) # use Euclidean distance
dmap = diffuse(D,neigen=3,eps.val=.01) # compute diffusion map & plot
plot(dmap)
dkmeans = diffusionKmeans(dmap, K=2)
col.dkmeans=ifelse(dkmeans$part==1,"red","blue")
scatterplot3d(Chainlink,color=col.dkmeans,
   main="Chainlink Data, colored by diff. K-means class")
table(dkmeans$part,lab.col)
# }