Package: clustering.evaluation
Superclasses: clustering.evaluation.ClusterCriterion
Silhouette criterion clustering evaluation object
clustering.evaluation.SilhouetteEvaluation
is
an object consisting of sample data, clustering data, and silhouette
criterion values used to evaluate the optimal number of data clusters.
Create a silhouette criterion clustering evaluation object using evalclusters
.
creates
a silhouette criterion clustering evaluation object.eva
= evalclusters(x
,clust
,'Silhouette')
creates
a silhouette criterion clustering evaluation object using additional
options specified by one or more namevalue pair arguments.eva
= evalclusters(x
,clust
,'Silhouette',Name,Value
)

Clustering algorithm used to cluster the input data, stored
as a valid clustering algorithm name or function handle. If the clustering
solutions are provided in the input, 

Prior probabilities for each cluster, stored as valid prior probability name. 

Silhouette values corresponding to each proposed number of clusters
in 

Name of the criterion used for clustering evaluation, stored as a valid criterion name. 

Criterion values corresponding to each proposed number of clusters
in 

Distance measure used for clustering data, stored as a valid distance measure name. 

List of the number of proposed clusters for which to compute criterion values, stored as a vector of positive integer values. 

Logical flag for excluded data, stored as a column vector of
logical values. If 

Number of observations in the data matrix 

Optimal number of clusters, stored as a positive integer value. 

Optimal clustering solution corresponding to 

Data used for clustering, stored as a matrix of numerical values. 
addK  Evaluate additional numbers of clusters 
compact  Compact clustering evaluation object 
plot  Plot clustering evaluation object criterion values 
[1] Kaufman L. and P. J. Rouseeuw. Finding Groups in Data: An Introduction to Cluster Analysis. Hoboken, NJ: John Wiley & Sons, Inc., 1990.
[2] Rouseeuw, P. J. "Silhouettes: a graphical aid to the interpretation and validation of cluster analysis." Journal of Computational and Applied Mathematics. Vol. 20, No. 1, 1987, pp. 53–65.