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.

Value

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

See also

Other tree space functions: MapTrees(), MappingQuality(), median.multiPhylo()

Author

Adapted by MRS from script by Nura Kawa

Examples

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