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