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

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. 
[1] Calinski, T., and J. Harabasz. “A dendrite method for cluster analysis.” Communications in Statistics. Vol. 3, No. 1, 1974, pp. 1–27.