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plot

Plot clustering evaluation object criterion values

    Description

    example

    plot(evaluation) displays a plot of the criterion values versus the number of clusters, based on the values in the clustering evaluation object evaluation.

    example

    h = plot(evaluation) returns a Line object. Use this object to inspect and adjust the properties of the plot line. For a list of properties, see Line Properties.

    Examples

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    Plot the criterion values versus the number of clusters for each clustering solution stored in a clustering evaluation object.

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

    load fisheriris

    Create a clustering evaluation object. Cluster the data using kmeans, and evaluate the optimal number of clusters using the Calinski-Harabasz criterion.

    rng("default") % For reproducibility
    evaluation = evalclusters(meas,"kmeans","CalinskiHarabasz","KList",1:6);

    Plot the Calinski-Harabasz criterion values for each number of clusters tested.

    plot(evaluation)

    Figure contains an axes object. The axes object contains 2 objects of type line.

    The plot shows that the highest Calinski-Harabasz value occurs at three clusters, suggesting that the optimal number of clusters is three.

    Cluster data using each of the four clustering evaluation criteria. For each criterion, create a plot of the criterion values and indicate the optimal number of clusters.

    Generate sample data containing random numbers from three multivariate distributions with different parameter values.

    rng("default") % For reproducibility
    n = 200;
    
    mu1 = [2 2];
    sigma1 = [0.9 -0.0255; -0.0255 0.9];
    
    mu2 = [5 5];
    sigma2 = [0.5 0; 0 0.3];
    
    mu3 = [-2 -2];
    sigma3 = [1 0; 0 0.9];
    
    X = [mvnrnd(mu1,sigma1,n); ...
         mvnrnd(mu2,sigma2,n); ...
         mvnrnd(mu3,sigma3,n)];

    Cluster the data using kmeans, and evaluate the optimal number of clusters using the Calinski-Harabasz, Davies-Bouldin, gap, and silhouette criteria.

    calinskiEvaluation = evalclusters(X,"kmeans","CalinskiHarabasz", ...
        "KList",1:6);
    daviesEvaluation = evalclusters(X,"kmeans","DaviesBouldin", ...
        "KList",1:6);
    gapEvaluation = evalclusters(X,"kmeans","gap","KList",1:6);
    silhouetteEvaluation = evalclusters(X,"kmeans","silhouette", ...
        "KList",1:6);

    For each clustering evaluation object, plot the criterion values for the number of proposed clusters. In each plot, change the color of the plot line and add a vertical line indicating the optimal number of clusters.

    t = tiledlayout(2,2);
    title(t,"Optimal Number of Clusters for Different Criteria")
    colors = lines(4);
    
    % Calinski-Harabasz Criterion Plot
    nexttile
    h1 = plot(calinskiEvaluation);
    h1.Color = colors(1,:);
    hold on
    xline(calinskiEvaluation.OptimalK,"--","Optimal K", ...
        "LabelVerticalAlignment","middle")
    hold off
    
    % Davies-Bouldin Criterion Plot
    nexttile
    h2 = plot(daviesEvaluation);
    h2.Color = colors(2,:);
    hold on
    xline(daviesEvaluation.OptimalK,"--","Optimal K", ...
        "LabelVerticalAlignment","middle")
    hold off
    
    % Gap Criterion Plot
    nexttile
    h3 = plot(gapEvaluation);
    h3.Color = colors(3,:);
    hold on
    xline(gapEvaluation.OptimalK,"--","Optimal K", ...
        "LabelVerticalAlignment","middle")
    hold off
    
    % Silhouette Criterion Plot
    nexttile
    h4 = plot(silhouetteEvaluation);
    h4.Color = colors(4,:);
    hold on
    xline(silhouetteEvaluation.OptimalK,"--","Optimal K", ...
        "LabelVerticalAlignment","middle")
    hold off

    Figure contains 4 axes objects. Axes object 1 contains 3 objects of type line, constantline. Axes object 2 contains 3 objects of type line, constantline. Axes object 3 contains 3 objects of type errorbar, line, constantline. Axes object 4 contains 3 objects of type line, constantline.

    The four plots indicate that the optimal number of clusters is three, regardless of the clustering criterion.

    Input Arguments

    collapse all

    Clustering evaluation data, specified as a CalinskiHarabaszEvaluation, DaviesBouldinEvaluation, GapEvaluation, or SilhouetteEvaluation clustering evaluation object. Create a clustering evaluation object by using evalclusters.

    Version History

    Introduced in R2013b