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)
```

- 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.

`SpectralEigens()`

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

Other tree space functions:
`MSTSegments()`

,
`MapTrees()`

,
`MappingQuality()`

,
`cluster-statistics`

,
`median.multiPhylo()`

```
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)
```