SpectralEigens function

Eigenvalues for spectral clustering

Spectral clustering emphasizes nearest neighbours when forming clusters; it avoids some of the issues that arise from clustering around means / medoids.

SpectralEigens(D, nn = 10L, nEig = 2L)

SpectralClustering(D, nn = 10L, nEig = 2L)

Arguments

• D: Square matrix or dist object containing Euclidean distances between data points.
• nn: Integer specifying number of nearest neighbours to consider
• nEig: Integer specifying number of eigenvectors to retain.

Returns

SpectralEigens() returns spectral eigenvalues that can then be clustered using a method of choice.

Examples

library("TreeTools", quietly = TRUE)
trees <- as.phylo(0:18, nTip = 8)
distances <- ClusteringInfoDistance(trees)
eigens <- SpectralEigens(distances)
# Perform clustering:
clusts <- KMeansPP(dist(eigens), k = 3)
plot(eigens, pch = 15, col = clusts$cluster)
plot(cmdscale(distances), pch = 15, col = clusts$cluster)

See Also

Other tree space functions: Islands(), MSTSegments(), MapTrees(), MappingQuality(), cluster-statistics, median.multiPhylo()

Author(s)

Adapted by MRS from script by Nura Kawa