### Class Definition: `ClusterCriterion`

statistics: obj = ClusterCriterion (x, clust, criterion)

A clustering evaluation object as created by `evalclusters`.

`ClusterCriterion` is a superclass for clustering evaluation objects as created by `evalclusters`.

List of public properties:

``ClusteringFunction``

a valid clustering funtion name or function handle. It can be empty if the clustering solutions are passed as an input matric.

``CriterionName``

a valid criterion name to evaluate the clustering solutions.

``CriterionValues``

a vector of values as generated by the evaluation criterion for each clustering solution.

``InspectedK``

the list of proposed cluster numbers.

``Missing``

a logical vector of missing observations. When there are `NaN` values in the data matrix, the corresponding observation is excluded.

``NumObservations``

the number of non-missing observations in the data matrix.

``OptimalK``

the optimal number of clusters.

``OptimalY``

the clustering solution corresponding to `OptimalK`.

``X``

the data matrix.

List of public methods:

``addK``

add a list of numbers of clusters to evaluate.

``compact``

return a compact clustering evaluation object. Not implemented

``plot``

plot the clustering evaluation values against the corresponding number of clusters.

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

Source Code: ClusterCriterion

### Method: `addK`

ClusterCriterion: obj = addK (obj, K)

Add a new cluster array to inspect the ClusterCriterion object.

### Method: `compact`

ClusterCriterion: obj = compact (obj)

Return a compact ClusterCriterion object (not implemented yet).

### Method: `plot`

ClusterCriterion: plot (obj)
ClusterCriterion: h = plot (obj)

Plot the evaluation results.

Plot the CriterionValues against InspectedK from the ClusterCriterion, obj, to the current plot. It can also return a handle to the current plot.