Davies-Bouldin criterion clustering evaluation object
DaviesBouldinEvaluation is an object consisting of sample data
X), clustering data (
OptimalY), and Davies-Bouldin
criterion values (
CriterionValues) used to
evaluate the optimal number of clusters (
OptimalK). The Davies-Bouldin
criterion is based on a ratio of within-cluster and between-cluster distances. The optimal
clustering solution has the smallest Davies-Bouldin index value. For more information, see
Create a Davies-Bouldin criterion clustering evaluation object by using the
evalclusters function and specifying the criterion as
You can then use
compact to create a compact version of the
Davies-Bouldin criterion clustering evaluation object. The function removes the contents of
Clustering Evaluation Properties
CriterionName — Name of criterion
This property is read-only.
Name of the criterion used for clustering evaluation, returned as
Sample Data Properties
Evaluate Clustering Solution Using Davies-Bouldin Criterion
Evaluate the optimal number of clusters using the Davies-Bouldin clustering evaluation criterion.
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)];
Evaluate the optimal number of clusters using the Davies-Bouldin criterion. Cluster the data using
evaluation = evalclusters(X,"kmeans","DaviesBouldin","KList",1:6)
evaluation = DaviesBouldinEvaluation with properties: NumObservations: 600 InspectedK: [1 2 3 4 5 6] CriterionValues: [NaN 0.4663 0.4454 0.8316 1.0444 0.9236] OptimalK: 3
OptimalK value indicates that, based on the Davies-Bouldin criterion, the optimal number of clusters is three.
Plot the Davies-Bouldin criterion values for each number of clusters tested.
The plot shows that the lowest Davies-Bouldin value occurs at three clusters, suggesting that the optimal number of clusters is three.
Create a grouped scatter plot to visually examine the suggested clusters.
clusters = evaluation.OptimalY; gscatter(X(:,1),X(:,2),clusters,,"xod")
The plot shows three distinct clusters within the data: cluster 1 in the lower-left corner, cluster 2 in the upper-right corner, and cluster 3 near the center of the plot.
The Davies-Bouldin criterion is based on a ratio of within-cluster and between-cluster distances. The Davies-Bouldin index is defined as
where Di,j is the within-to-between cluster distance ratio for the ith and jth clusters.
In mathematical terms,
is the average distance between each point in the ith cluster and the centroid of the ith cluster. is the average distance between each point in the jth cluster and the centroid of the jth cluster. is the Euclidean distance between the centroids of the ith and jth clusters.
The maximum value of Di,j represents the worst-case within-to-between cluster ratio for cluster i. The optimal clustering solution has the smallest Davies-Bouldin index value.
 Davies, D. L., and D. W. Bouldin. “A Cluster Separation Measure.” IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. PAMI-1, No. 2, 1979, pp. 224–227.
Introduced in R2013b