## Partition Data Using Spectral Clustering

This topic provides an introduction to spectral clustering and an example that estimates the number of clusters and performs spectral clustering.

### Introduction to Spectral Clustering

Spectral clustering is a graph-based algorithm for partitioning data points, or
observations, into *k* clusters. The Statistics and Machine Learning Toolbox™ function `spectralcluster`

performs clustering on an input data matrix or on a
similarity matrix of a similarity graph derived from the data.
`spectralcluster`

returns the cluster indices, a matrix
containing *k* eigenvectors of the Laplacian
matrix, and a vector of eigenvalues corresponding to the
eigenvectors.

`spectralcluster`

requires you to specify the number of
clusters *k*. However, you can verify that your estimate for
*k* is correct by using one of these methods:

Count the number of zero eigenvalues of the Laplacian matrix. The multiplicity of the zero eigenvalues is an indicator of the number of clusters in your data.

Find the number of connected components in your similarity matrix by using the MATLAB

^{®}function`conncomp`

.

### Algorithm Description

*Spectral clustering* is a graph-based algorithm for finding
*k* arbitrarily shaped clusters in data. The technique involves
representing the data in a low dimension. In the low dimension, clusters in the data
are more widely separated, enabling you to use algorithms such as
*k*-means or *k*-medoids clustering. This low
dimension is based on the eigenvectors corresponding to the *k*
smallest eigenvalues of a Laplacian matrix. A Laplacian matrix is one way of
representing a similarity graph that models the local neighborhood relationships
between data points as an undirected graph. The spectral clustering algorithm
derives a similarity matrix of a similarity graph from your data, finds the
Laplacian matrix, and uses the Laplacian matrix to find *k*
eigenvectors for splitting the similarity graph into *k*
partitions. You can use spectral clustering when you know the number of clusters,
but the algorithm also provides a way to estimate the number of clusters in your
data.

By default, the algorithm for `spectralcluster`

computes the normalized random-walk Laplacian matrix
using the method described by Shi-Malik [1].
`spectralcluster`

also supports the unnormalized Laplacian
matrix and the normalized symmetric Laplacian matrix which uses the Ng-Jordan-Weiss
method [2]. The
`spectralcluster`

function 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*.

### Estimate Number of Clusters and Perform Spectral Clustering

This example demonstrates two approaches for performing spectral clustering.

The first approach estimates the number of clusters using the eigenvalues of the Laplacian matrix and performs spectral clustering on the data set.

The second approach estimates the number of clusters using the similarity graph and performs spectral clustering on the similarity matrix.

**Generate Sample Data**

Randomly generate a sample data set with three well-separated clusters, each containing 20 points.

rng('default'); % For reproducibility n = 20; X = [randn(n,2)*0.5+3; randn(n,2)*0.5; randn(n,2)*0.5-3];

**Perform Spectral Clustering on Data**

Estimate the number of clusters in the data by using the eigenvalues of the Laplacian matrix, and perform spectral clustering on the data set.

Compute the five smallest eigenvalues (in magnitude) of the Laplacian matrix by using the `spectralcluster`

function. By default, the function uses the normalized random-walk Laplacian matrix.

[~,V_temp,D_temp] = spectralcluster(X,5)

`V_temp = `*60×5*
0.2236 -0.0000 0.0000 0.1534 -0.0000
0.2236 -0.0000 0.0000 -0.3093 0.0000
0.2236 -0.0000 0.0000 0.2225 -0.0000
0.2236 -0.0000 0.0000 0.1776 -0.0000
0.2236 -0.0000 0.0000 0.1331 -0.0000
0.2236 -0.0000 0.0000 0.2176 -0.0000
0.2236 -0.0000 0.0000 0.1967 0.0000
0.2236 -0.0000 0.0000 -0.0088 0.0000
0.2236 -0.0000 0.0000 -0.2844 0.0000
0.2236 -0.0000 0.0000 -0.3275 0.0000
⋮

`D_temp = `*5×1*
-0.0000
-0.0000
-0.0000
0.0876
0.1653

Only the first three eigenvalues are approximately zero. The number of zero eigenvalues is a good indicator of the number of connected components in a similarity graph and, therefore, is a good estimate of the number of clusters in your data. So, `k=3`

is a good estimate of the number of clusters in `X`

.

Perform spectral clustering on observations by using the `spectralcluster`

function. Specify `k=3`

clusters.

k = 3; [idx1,V,D] = spectralcluster(X,k)

`idx1 = `*60×1*
1
1
1
1
1
1
1
1
1
1
⋮

`V = `*60×3*
0.2236 -0.0000 -0.0000
0.2236 -0.0000 -0.0000
0.2236 -0.0000 -0.0000
0.2236 -0.0000 -0.0000
0.2236 -0.0000 -0.0000
0.2236 -0.0000 -0.0000
0.2236 -0.0000 -0.0000
0.2236 -0.0000 -0.0000
0.2236 -0.0000 -0.0000
0.2236 -0.0000 -0.0000
⋮

D =3×110^{-16}× -0.0740 -0.1589 -0.3613

Elements of `D`

correspond to the three smallest eigenvalues of the Laplacian matrix. The columns of `V`

contain the eigenvectors corresponding to the eigenvalues in `D`

. For well-separated clusters, the eigenvectors are indicator vectors. The eigenvectors have values of zero (or close to zero) for points that do not belong to a particular cluster, and nonzero values for points that belong to a particular cluster.

Visualize the result of clustering.

gscatter(X(:,1),X(:,2),idx1);

The `spectralcluster`

function correctly identifies the three clusters in the data set.

Instead of using the `spectralcluster`

function again, you can pass `V_temp`

to the `kmeans`

function to cluster the data points.

idx2 = kmeans(V_temp(:,1:3),3); gscatter(X(:,1),X(:,2),idx2);

The order of cluster assignments in `idx1`

and `idx2`

is different even though the data points are clustered in the same way.

**Perform Spectral **Clustering** on Similarity Matrix**

Estimate the number of clusters using the similarity graph and perform spectral clustering on the similarity matrix.

Find the distance between each pair of observations in `X`

by using the `pdist`

and `squareform`

functions with the default Euclidean distance metric.

dist_temp = pdist(X); dist = squareform(dist_temp);

Construct the similarity matrix from the pairwise distance and confirm that the similarity matrix is symmetric.

S = exp(-dist.^2); issymmetric(S)

`ans = `*logical*
1

Limit the similarity values to `0.5`

so that the similarity graph connects only points whose pairwise distances are smaller than the search radius.

S_eps = S; S_eps(S_eps<0.5) = 0;

Create a `graph`

object from `S`

.

G_eps = graph(S_eps);

Visualize the similarity graph.

plot(G_eps)

Identify the number of connected components in graph `G_eps`

by using the `unique`

and `conncomp`

functions.

unique(conncomp(G_eps))

`ans = `*1×3*
1 2 3

The similarity graph shows three sets of connected components. The number of connected components in the similarity graph is a good estimate of the number of clusters in your data. Therefore, `k=3`

is a good choice for the number of clusters in `X`

.

Perform spectral clustering on the similarity matrix derived from the data set `X`

.

k = 3; idx3 = spectralcluster(S_eps,k,'Distance','precomputed'); gscatter(X(:,1),X(:,2),idx3);

The `spectralcluster`

function correctly identifies the three clusters in the data set.

## References

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

[2] 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.

## See Also

`spectralcluster`

| `pdist2`

| `pdist`

| `squareform`

| `adjacency`

| `conncomp`