Rdimtools (version 1.0.0)

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))))

Arguments

X

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

kmin

minimal number of neighborhood size to search over.

Value

a named list containing containing

estdim

estimated intrinsic dimension.

References

eriksson_estimating_2012Rdimtools

Examples

# NOT RUN {
## 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
sprintf("* est.clustering : estimated dimension with kmin=5  is %.2f.",out1$estdim)
sprintf("* est.clustering : estimated dimension with kmin=25 is %.2f.",out2$estdim)
sprintf("* est.clustering : estimated dimension with kmin=50 is %.2f.",out3$estdim)
# }
# NOT RUN {
# }