Diffusion K-means
Clusters a data set based on its diffusion coordinates.
diffusionKmeans(dmap, K, params = c(), Niter = 10, epsilon = 0.001)
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 |
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.
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. |
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
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.