Rdimtools (version 1.1.2)

est.clustering: Intrinsic Dimension Estimation via Clustering

Description

Instead of directly using neighborhood information, est.clustering adopts hierarchical neighborhood information using hclust by recursively merging leafs over the range of radii.

Usage

est.clustering(X, kmin = round(sqrt(nrow(X))))

Value

a named list containing containing

estdim

estimated intrinsic dimension.

Arguments

X

an \((n\times p)\) matrix or data frame whose rows are observations.

kmin

minimal number of neighborhood size to search over.

Author

Kisung You

References

eriksson_estimating_2012Rdimtools

Examples

# \donttest{
## create 'swiss' roll dataset
X = aux.gensamples(dname="swiss")

## try different k values
out1 = est.clustering(X, kmin=5)
out2 = est.clustering(X, kmin=25)
out3 = est.clustering(X, kmin=50)

## print the results
line1 = paste0("* est.clustering : kmin=5  gives ",round(out1$estdim,2))
line2 = paste0("* est.clustering : kmin=25 gives ",round(out2$estdim,2))
line3 = paste0("* est.clustering : kmin=50 gives ",round(out3$estdim,2))
cat(paste0(line1,"\n",line2,"\n",line3))
# }