SpectralEigens

SpectralClustering

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

Implements measures of tree similarity, including
information-based generalized Robinson-Foulds distances
(Phylogenetic Information Distance, Clustering Information Distance,
Matching Split Information Distance; Smith 2020)
<doi:10.1093/bioinformatics/btaa614>;
Jaccard-Robinson-Foulds distances (Bocker et al. 2013)
<doi:10.1007/978-3-642-40453-5_13>,
including the Nye et al. (2006) metric <doi:10.1093/bioinformatics/bti720>;
the Matching Split Distance (Bogdanowicz & Giaro 2012)
<doi:10.1109/TCBB.2011.48>;
Maximum Agreement Subtree distances;
the Kendall-Colijn (2016) distance <doi:10.1093/molbev/msw124>, and the
Nearest Neighbour Interchange (NNI) distance, approximated per Li et al.
(1996) <doi:10.1007/3-540-61332-3_168>.
Includes tools for visualizing mappings of tree space (Smith 2022)
<doi:10.1093/sysbio/syab100>,
for identifying islands of trees (Silva and Wilkinson 2021)
<doi:10.1093/sysbio/syab015>,
for calculating the median of sets of trees,
and for computing the information content of trees and splits.

Martin R. Smith

TreeDist

Calculate and Map Distances Between Phylogenetic Trees

Roy Jonker

Yong Yang

Yi Cao

SpectralEigens function

<dl><dt>D</dt>
<dd>Square matrix or <code>dist</code> object containing Euclidean distances
between data points.</dd>
<dt>nn</dt>
<dd>Integer specifying number of nearest neighbours to consider</dd>
<dt>nEig</dt>
<dd>Integer specifying number of eigenvectors to retain.</dd></dl>

Arguments

Adapted by MRS from script by <a href="https://rpubs.com/nurakawa/spectral-clustering">Nura Kawa</a>

Author

Eigenvalues for spectral clustering — SpectralEigens

<dl>

<dt>D</dt>
<dd>Square matrix or <code>dist</code> object containing Euclidean distances
between data points.</dd>


<dt>nn</dt>
<dd>Integer specifying number of nearest neighbours to consider</dd>


<dt>nEig</dt>
<dd>Integer specifying number of eigenvectors to retain.</dd>

</dl>

Adapted by MRS from script by <a href='https://rpubs.com/nurakawa/spectral-clustering'>Nura Kawa</a>

SpectralEigens: Eigenvalues for spectral clustering

Description

Usage

Value

Arguments

Author

See Also

Examples