The SilhouetteEvaluation class implements an object to evaluate clustering solutions using the silhouette criterion. A SilhouetteEvaluation object is a ClusterCriterion object that computes silhouette values for clustering solutions and selects the best number of clusters as the one with the highest average silhouette value.

Create a SilhouetteEvaluation object by using the evalclusters function or the class constructor.

List of public properties specific to SilhouetteEvaluation:

Distance

A valid distance metric name (string), a function handle, or a numeric vector as returned by pdist. This specifies how pairwise distances are computed.

ClusterPriors

A character vector specifying how to evaluate silhouette values across clusters: 'empirical' (default) uses empirical cluster priors, or 'equal' treats clusters equally.

ClusterSilhouettes

A cell array containing silhouette values for each observation for each inspected cluster number.

The best clustering solution according to the silhouette criterion is the one that yields the highest average silhouette value.

See also: evalclusters, ClusterCriterion, CalinskiHarabaszEvaluation, DaviesBouldinEvaluation, GapEvaluation

Source Code: SilhouetteEvaluation