ClusterCriterion
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 function name or function handle. It can be empty if the clustering solutions are passed as an input matrix.
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
ClusterCriterion.ClusteringFunction
is not documented.
ClusterCriterion.CriterionName
is not documented.
ClusterCriterion.CriterionValues
is not documented.
ClusterCriterion.InspectedK
is not documented.
ClusterCriterion.Missing
is not documented.
ClusterCriterion.NumObservations
is not documented.
ClusterCriterion.OptimalK
is not documented.
ClusterCriterion.OptimalY
is not documented.
ClusterCriterion.X
is not documented.
Add a new cluster array to inspect the ClusterCriterion object.
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.
Return a compact ClusterCriterion object (not implemented yet).