# cluster

Construct agglomerative clusters from linkages

## Syntax

## Description

defines clusters from an agglomerative hierarchical cluster tree `T`

= cluster(`Z`

,`'Cutoff'`

,`C`

)`Z`

.
The input `Z`

is the output of the `linkage`

function for an input data matrix `X`

.
`cluster`

cuts `Z`

into clusters, using
`C`

as a threshold for the inconsistency coefficients (or `inconsistent`

values) of nodes in the tree. The output `T`

contains cluster assignments of each observation (row of `X`

).

## Examples

### Define Clusters by Specifying Depth

Perform agglomerative clustering on randomly generated data by evaluating inconsistent values to a depth of four below each node.

Randomly generate the sample data.

rng('default'); % For reproducibility X = [(randn(20,2)*0.75)+1; (randn(20,2)*0.25)-1];

Create a scatter plot of the data.

```
scatter(X(:,1),X(:,2));
title('Randomly Generated Data');
```

Create a hierarchical cluster tree using the `ward`

linkage method.

`Z = linkage(X,'ward');`

Create a dendrogram plot of the data.

dendrogram(Z)

The scatter plot and the dendrogram plot seem to show two clusters in the data.

Cluster the data using a threshold of 3 for the inconsistency coefficient and looking to a depth of 4 below each node. Plot the resulting clusters.

T = cluster(Z,'cutoff',3,'Depth',4); gscatter(X(:,1),X(:,2),T)

`cluster`

identifies two clusters in the data.

### Cluster Data Using Distance Criterion

Perform agglomerative clustering on the `fisheriris`

data set using `'distance'`

as the criterion for defining clusters. Visualize the cluster assignments of the data.

Load the `fisheriris`

data set.

`load fisheriris`

Visualize a 2-D scatter plot of the data using species as the grouping variable. Specify marker colors and marker symbols for the three different species.

gscatter(meas(:,1),meas(:,2),species,'rgb','do*') title("Actual Clusters of Fisher's Iris Data")

Create a hierarchical cluster tree using the `'average'`

method and the `'chebychev'`

metric.

Z = linkage(meas,'average','chebychev');

Cluster the data using a threshold of 1.5 for the `'distance'`

criterion.

T = cluster(Z,'cutoff',1.5,'Criterion','distance')

`T = `*150×1*
2
2
2
2
2
2
2
2
2
2
⋮

`T`

contains numbers that correspond to the cluster assignments. Find the number of classes that `cluster`

identifies.

length(unique(T))

ans = 3

`cluster`

identifies three classes for the specified values of `cutoff`

and `Criterion`

.

Visualize a 2-D scatter plot of the clustering results using `T`

as the grouping variable. Specify marker colors and marker symbols for the three different classes.

gscatter(meas(:,1),meas(:,2),T,'rgb','do*') title("Cluster Assignments of Fisher's Iris Data")

Clustering correctly identifies the setosa class (class 2) as belonging to a distinct cluster, but poorly distinguishes between the versicolor and virginica classes (classes 1 and 3, respectively). Note that the scatter plot labels the classes using the numbers contained in `T`

.

### Compare Cluster Assignments to Classes

Find a maximum of three clusters in the `fisheriris`

data set and compare cluster assignments of the flowers to their known classification.

Load the sample data.

`load fisheriris`

Create a hierarchical cluster tree using the `'average'`

method and the `'chebychev'`

metric.

Z = linkage(meas,'average','chebychev');

Find a maximum of three clusters in the data.

`T = cluster(Z,'maxclust',3);`

Create a dendrogram plot of `Z`

. To see the three clusters, use `'ColorThreshold'`

with a cutoff halfway between the third-from-last and second-from-last linkages.

```
cutoff = median([Z(end-2,3) Z(end-1,3)]);
dendrogram(Z,'ColorThreshold',cutoff)
```

Display the last two rows of `Z`

to see how the three clusters are combined into one. `linkage`

combines the 293rd (blue) cluster with the 297th (red) cluster to form the 298th cluster with a linkage of `1.7583`

. `linkage`

then combines the 296th (green) cluster with the 298th cluster.

lastTwo = Z(end-1:end,:)

`lastTwo = `*2×3*
293.0000 297.0000 1.7583
296.0000 298.0000 3.4445

See how the cluster assignments correspond to the three species. For example, one of the clusters contains `50`

flowers of the second species and `40`

flowers of the third species.

crosstab(T,species)

`ans = `*3×3*
0 0 10
0 50 40
50 0 0

### Cluster Data and Plot Result

Randomly generate sample data with 20,000 observations.

rng('default') % For reproducibility X = rand(20000,3);

Create a hierarchical cluster tree using the `ward`

linkage method. In this case, the `'SaveMemory'`

option of the `clusterdata`

function is set to `'on'`

by default. In general, specify the best value for `'SaveMemory'`

based on the dimensions of `X`

and the available memory.

`Z = linkage(X,'ward');`

Cluster the data into a maximum of four groups and plot the result.

```
c = cluster(Z,'Maxclust',4);
scatter3(X(:,1),X(:,2),X(:,3),10,c)
```

`cluster`

identifies four groups in the data.

## Input Arguments

`Z`

— Agglomerative hierarchical cluster tree

numeric matrix

Agglomerative hierarchical cluster tree that is the output of the `linkage`

function, specified as a numeric matrix. For an input data matrix
`X`

with *m* rows (or observations),
`linkage`

returns an (*m* – 1)-by-3 matrix `Z`

. For an explanation of how
`linkage`

creates the cluster tree, see `Z`

.

**Example: **`Z = linkage(X)`

, where `X`

is an input
data matrix

**Data Types: **`single`

| `double`

`C`

— Threshold for defining clusters

positive scalar | vector of positive scalars

Threshold for defining clusters, specified as a positive scalar or a vector of
positive scalars. `cluster`

uses `C`

as a
threshold for either the heights or the inconsistency coefficients of nodes, depending
on the `criterion`

for defining clusters in a hierarchical cluster tree.

If the criterion for defining clusters is

`'distance'`

, then`cluster`

groups all leaves at or below a node into a cluster, provided that the height of the node is less than`C`

.If the criterion for defining clusters is

`'inconsistent'`

, then the`inconsistent`

values of a node and all its subnodes must be less than`C`

for`cluster`

to group them into a cluster.`cluster`

begins from the root of the cluster tree`Z`

and steps down through the tree until it encounters a node whose`inconsistent`

value is less than the threshold`C`

, and whose subnodes (or descendants) have inconsistent values less than`C`

. Then`cluster`

groups all leaves at or below the node into a cluster (or a singleton if the node itself is a leaf).`cluster`

follows every branch in the tree until all leaf nodes are in clusters.

**Example: **`cluster(Z,'Cutoff',0.5)`

**Data Types: **`single`

| `double`

`D`

— Depth for computing inconsistent values

2 (default) | numeric scalar

Depth for computing inconsistent values, specified as a numeric scalar.
`cluster`

evaluates inconsistent values by looking to a depth
`D`

below each node.

**Example: **`cluster(Z,'Cutoff',0.5,'Depth',3)`

**Data Types: **`single`

| `double`

`criterion`

— Criterion for defining clusters

`'inconsistent'`

(default) | `'distance'`

Criterion for defining clusters, specified as `'inconsistent'`

or
`'distance'`

.

If the criterion for defining clusters is `'distance'`

, then
`cluster`

groups all leaves at or below a node into a cluster (or a
singleton if the node itself is a leaf), provided that the height of the node is less
than `C`

. The height of a node in a tree represents the distance
between the two subnodes that are merged at that node. Specifying
`'distance'`

results in clusters that correspond to a horizontal
slice of the `dendrogram`

plot of
`Z`

.

If the criterion for defining clusters is `'inconsistent'`

, then
`cluster`

groups a node and all its subnodes into a cluster,
provided that the inconsistency coefficients (or `inconsistent`

values) of the node and subnodes are less than
`C`

. Specifying `'inconsistent'`

is equivalent to
`cluster(Z,'Cutoff',C)`

.

**Example: **`cluster(Z,'Cutoff',0.5,'Criterion','distance')`

**Data Types: **`char`

| `string`

`N`

— Maximum number of clusters

positive integer | vector of positive integers

Maximum number of clusters to form, specified as a positive integer or a vector of
positive integers. `cluster`

constructs a maximum of
`N`

clusters, using `'distance'`

as the
criterion for defining clusters. The height of each node in the tree represents the
distance between the two subnodes merged at that node. `cluster`

finds the smallest height at which a horizontal cut through the tree will leave
`N`

or fewer clusters. See Specify Arbitrary Clusters for more details.

**Example: **`cluster(Z,'MaxClust',5)`

**Data Types: **`single`

| `double`

## Output Arguments

`T`

— Cluster assignment

numeric vector | numeric matrix

Cluster assignment, returned as a numeric vector or matrix. For the (*m* – 1)-by-3 hierarchical cluster tree `Z`

(the output of
`linkage`

given input `X`

),
`T`

contains the cluster assignments of the *m*
rows (observations) of `X`

.

The size of `T`

depends on the corresponding size of
`C`

or `N`

.

If

`C`

is a positive scalar, then`T`

is a vector of length*m*.If

`N`

is a positive integer, then`T`

is a vector of length*m*.If

`C`

is a length*l*vector of positive scalars, then`T`

is an*m*-by-*l*matrix with one column per value in`C`

.If

`N`

is a length*l*vector of positive integers, then`T`

is an*m*-by-*l*matrix with one column per value in`N`

.

## Alternative Functionality

If you have an input data matrix `X`

, you can use `clusterdata`

to perform agglomerative clustering and return cluster indices for
each observation (row) in `X`

. The `clusterdata`

function
performs all the necessary steps for you, so you do not need to execute the `pdist`

, `linkage`

, and `cluster`

functions separately.

**Introduced before R2006a**

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