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Cluster Visualization and Evaluation

Plot clusters of data and evaluate optimal number of clusters

Cluster analysis organizes data into groups based on similarities between the data points. Sometimes the data contains natural divisions that indicate the appropriate number of clusters. Other times, the data does not contain natural divisions, or the natural divisions are unknown. In such a case, you determine the optimal number of clusters to group your data.

To determine how well the data fits into a particular number of clusters, compute index values using different evaluation criteria, such as gap or silhouette. Visualize clusters by creating a dendrogram plot to display a hierarchical binary cluster tree. Optimize the leaf order to maximize the sum of the similarities between adjacent leaves. For grouped data with multiple measurements for each group, create a dendrogram plot based on the group means computed using a multivariate analysis of variance (MANOVA).

Live Editor Tasks

Cluster DataCluster data using k-means or hierarchical clustering in the Live Editor (Since R2021b)


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dendrogramDendrogram plot
optimalleaforderOptimal leaf ordering for hierarchical clustering
manovaclusterDendrogram of group mean clusters following MANOVA
silhouetteSilhouette plot
evalclustersEvaluate clustering solutions
addKEvaluate additional numbers of clusters
compactCompact clustering evaluation object
increaseBIncrease reference data sets
plot Plot clustering evaluation object criterion values


CalinskiHarabaszEvaluationCalinski-Harabasz criterion clustering evaluation object
DaviesBouldinEvaluationDavies-Bouldin criterion clustering evaluation object
GapEvaluationGap criterion clustering evaluation object
SilhouetteEvaluationSilhouette criterion clustering evaluation object