# spectralcluster

## Syntax

## Description

partitions observations in the `idx`

= spectralcluster(`X`

,`k`

)*n*-by-*p* data matrix
`X`

into `k`

clusters using the spectral clustering
algorithm (see Algorithms).
`spectralcluster`

returns an *n*-by-1 vector
`idx`

containing cluster indices of each observation.

returns a vector of cluster indices for `idx`

= spectralcluster(`S`

,`k`

,`'Distance'`

,'precomputed')`S`

, the similarity matrix (or adjacency matrix) of a similarity graph. `S`

can be the output of `adjacency`

.

To use a similarity matrix as the first input, you must specify
`'Distance','precomputed'`

.

specifies additional options using one or more name-value pair arguments in addition to
the input arguments in previous syntaxes. For example, you can specify
`idx`

= spectralcluster(___,`Name,Value`

)`'SimilarityGraph','epsilon'`

to construct a similarity graph using the
radius search method.

`[`

also returns the eigenvectors `idx`

,`V`

] = spectralcluster(___)`V`

corresponding to the
`k`

smallest eigenvalues of the Laplacian
matrix.

## Examples

## Input Arguments

## Output Arguments

## More About

## Tips

Consider using spectral clustering when the clusters in your data do not naturally correspond to convex regions.

From the spectral clustering algorithm, you can estimate the number of clusters

`k`

as:For an example, see Estimate Number of Clusters and Perform Spectral Clustering.

## Algorithms

Spectral clustering is a graph-based algorithm for clustering data points (or observations
in `X`

). The algorithm involves constructing a graph, finding its Laplacian matrix, and
using this matrix to find *k* eigenvectors to split the graph
*k* ways. By default, the algorithm for
`spectralcluster`

computes the normalized random-walk Laplacian matrix
using the method described by Shi-Malik [2].
`spectralcluster`

also supports the unnormalized Laplacian matrix and the
normalized symmetric Laplacian matrix which uses the Ng-Jordan-Weiss method [3].
`spectralcluster`

implements clustering as follows:

For each data point in

`X`

, define a local neighborhood using either the radius search method or nearest neighbor method, as specified by the`'SimilarityGraph'`

name-value pair argument (see Similarity Graph). Then, find the pairwise distances $$Dis{t}_{i,j}$$ for all points*i*and*j*in the neighborhood.Convert the distances to similarity measures using the kernel transformation $${S}_{i,j}=\mathrm{exp}\left(-{\left(\frac{Dis{t}_{i,j}}{\sigma}\right)}^{2}\right)$$. The matrix

*S*is the similarity matrix, and*σ*is the scale factor for the kernel, as specified using the`'KernelScale'`

name-value pair argument.Calculate the unnormalized Laplacian matrix

*L*, the normalized random-walk Laplacian matrix*L*, or the normalized symmetric Laplacian matrix_{rw}*L*, depending on the value of the_{s}`'LaplacianNormalization'`

name-value pair argument.Create a matrix $$V\in {\mathbb{R}}^{n\times k}$$ containing columns $${v}_{1},\dots ,{v}_{k}$$, where the columns are the

*k*eigenvectors that correspond to the*k*smallest eigenvalues of the Laplacian matrix. If using*L*, normalize each row of_{s}*V*to have unit length.Treating each row of

*V*as a point, cluster the*n*points using*k*-means clustering (default) or*k*-medoids clustering, as specified by the`'ClusterMethod'`

name-value pair argument.Assign the original points in

`X`

to the same clusters as their corresponding rows in*V*.

## References

[2] Shi, J., and J. Malik.
“Normalized cuts and image segmentation.” *IEEE Transactions on
Pattern Analysis and Machine Intelligence*. Vol. 22, 2000, pp.
888–905.

[3] Ng, A.Y., M. Jordan, and Y. Weiss.
“On spectral clustering: Analysis and an algorithm.” In *Proceedings
of the Advances in Neural Information Processing Systems 14*. MIT Press, 2001,
pp. 849–856.

## Version History

**Introduced in R2019b**