The evalclusters function determines the optimal number of clusters for you. ‘linkage’ will use agglomerative clustering as the algorithm with the ‘ward’ distance. You have a choice of cluster evaluation criteria, ‘CalinksiHarabasz’ and ‘DaviesBouldin’ compare the between and within cluster distances in slightly different ways, and there is also a ‘gap’ and ‘silhouette’ criterion. The output object from evalclusters contains the criterion values for each number of clusters along with the optimal value.
So for example,
eva = evalclusters(X,'linkage','CalinskiHarabasz','KList',[2:6])
with input data in the matrix X will evaluate 2-6 clusters and provide the following output:
InspectedK: [2 3 4 5 6]
CriterionValues: [180.0914 300.2080 254.8927 220.7171 199.2285]
OptimalK: 3
