Cluster Data

Cluster data using k-means algorithm in the Live Editor

Since R2021b

Description

The Cluster Data Live Editor Task enables you to interactively perform k-means clustering. The task generates MATLAB^® code for your live script and returns the resulting cluster indices and the cluster centroid locations to the MATLAB workspace.

You can:

Determine the optimal number of clusters for your data manually by selecting the number of clusters or automatically by specifying criteria such as gap values, silhouette values, Davies-Bouldin index values, and Calinski-Harabasz index values.
Customize the parameters for clustering your data, including the distance metric and the number of replicates.
Automatically visualize the clustered data.

For general information about Live Editor tasks, see Add Interactive Tasks to a Live Script.

Open the Task

To add the Cluster Data task to a live script:

On the Live Editor tab, select Task > Cluster Data.
In a code block in the live script, type a relevant keyword, such as clustering or kmeans. Select Cluster Data from the suggested command completions.

Examples

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Cluster Data with Specified Number of Clusters By Using Live Editor Task

This example shows how to use the Cluster Data task to interactively perform k-means clustering for a specified number of clusters.

Load the sample data. The data contains length and width measurements from the sepals and petals of three species of iris flowers.

load fisheriris

Open the Cluster Data task. To open the task, begin typing the keyword clustering in a code block and select Cluster Data from the suggested command completions.

Drop down list showing suggested command completions. The third suggestion in the list is for the Cluster Data Task, and is selected.

Cluster the data into two clusters.

Select the meas variable as the input data.
Set the number of clusters to 2.
In the Live Editor tab, press the Run button to run the task.

MATLAB displays the clustered data and the cluster means in a scatter plot.

Cluster Data task showing the selected parameters and the resulting scatter plot with the sample data divided into two clusters.

Increase the number of clusters to 3 and rerun the task. MATLAB displays the updated clustered data and the cluster means in a scatter plot.

Cluster Data task showing the selected parameters and the resulting scatter plot with the sample data divided into three clusters.

The task generates code in your live script. The generated code reflects the parameters and options that you select, and includes code to generate the scatter plot. To see the generated code, click the at the bottom of the task parameter area. The task expands to display the generated code.

Generated code for the Cluster Data task. The code uses the kmeans function to cluster the data and the scatter function to display the results.

By default, the generated code uses clusterIndices and centroids as the name of the output variables returned to the MATLAB workspace. The clusterIndices vector is a numeric column vector containing the cluster indices. Each row in clusterIndices indicates the cluster assignment of the corresponding observation. The centroids matrix is a numeric matrix containing the cluster centroid locations. To specify a different output variable name, enter a new name in the summary line at the top of the task. For instance, change the two variable names to c_indices and c_locations.

First row of the Cluster Data task with the renamed output c_indices and c_locations circled in red.

When the task runs, the generated code is updated to reflect the new variable names. The new variables c_indices and c_locations appear in the MATLAB workspace.

Evaluate the Optimal Number of Clusters By Using Live Editor Task

This example shows how to use the Cluster Data task to interactively evaluate clustering solutions based on selected criteria.

Load the sample data. The data contains length and width measurements from the sepals and petals of three species of iris flowers.

load fisheriris

Open the Cluster Data task. To open the task, begin typing the keyword clustering in a code block and select Cluster Data from the suggested command completions.

Drop down list showing suggested command completions. The third suggestion in the list is for the Cluster Data Task, and is selected.

Evaluate the optimal number of clusters.

Select the meas variable as the input data.
Set the number of clusters selection method to Optimal.
Set the range min and max to 2 and 6.
In the Live Editor tab, press the Run button to run the task.

MATLAB displays a bar chart with evaluation results, indicating that, based on the Calinski-Harabasz criterion, the optimal number of clusters is 3. A scatter plot shows the clustered data and the cluster means using the optimal number of clusters, 3. Your results may differ.

Cluster Data task showing the selected parameters and two charts. The chart is a bar chart displaying the evaluation results for each cluster number and the second chart is a scatter plot with the sample data divided into three clusters.

Related Examples

Parameters

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`Input data` — Data to cluster
numeric matrix

Specify the data to cluster by selecting a variable from the available workspace variables. The variable must be a numeric matrix to appear in the list.

`Selection Method` — Cluster selection method
`Manual` (default) | `Optimal`

Specify the method for determining the optimal number of clusters for your data.

Manual — Specify the number of clusters to group your data into manually.
Optimal— Use the evalclusters function to find the optimal number of clusters based on criteria such as gap values, silhouette values, Davies-Bouldin index values, and Calinski-Harabasz index values.

`Range` — List of number of clusters to evaluate
`2:5` (default) | min and max positive integer values

Specify the list of number of clusters to evaluate as a range consisting of a min value and a max value. For example, if you specify a min value of 2 and a max value of 6, the task evaluates the number of clusters 2, 3, 4, 5, and 6 to determine the optimal number.

`Plots to show` — Plots to show results with
check boxes

To display the clustered data, select from the available options:

Select 2D scatter plot (PCA) to display the principle components of the clustered data in a 2D scatter plot. The Cluster Data task uses the gscatter function to create the scatter plot.
Select Matrix of scatter plots to display the clustered data in a matrix of scatter plots. When you select Matrix of scatter plots, a list appears to the right of the check box. Each item in the list represents a column in the specified input data. Press the Ctrl key and select a maximum of four input data columns from the list. The Cluster Data task uses the pca and gplotmatrix functions to create the matrix of scatter plots from the selected columns.
The scatter plots in the matrix compare the selected input data columns across cluster indices. The diagonal plots in the matrix are histograms showing the distribution of the selected columns for each cluster indices.

Tips

By default, the Cluster Data task does not automatically run when you modify the task parameters. To have the task run automatically after any change, select the autorun button at the top-right of the task. If your dataset is large, do not enable this option.