CalinskiHarabaszEvaluation
statistics: CalinskiHarabaszEvaluation
Calinski-Harabasz clustering evaluation.
A CalinskiHarabaszEvaluation object contains the results of
evaluating clustering solutions using the Calinski-Harabasz criterion.
The Calinski-Harabasz index (also known as the Variance Ratio Criterion) is determined by the ratio of the between-cluster sum of squares (SSB) to the within-cluster sum of squares (SSW). A higher Calinski-Harabasz index value indicates a better clustering solution, implying that clusters are dense and well-separated.
Create a CalinskiHarabaszEvaluation object by using the
evalclusters function with the "CalinskiHarabasz" criterion.
See also: evalclusters, ClusterCriterion, DaviesBouldinEvaluation, GapEvaluation, SilhouetteEvaluation
Source Code: CalinskiHarabaszEvaluation
CalinskiHarabaszEvaluation: obj = addK (obj, K)
addK (obj, K) evaluates clustering solutions for the
number of clusters specified in the vector K and adds them to the
CalinskiHarabaszEvaluation object obj.
See also: CalinskiHarabaszEvaluation, evalclusters
CalinskiHarabaszEvaluation: plot (obj)
CalinskiHarabaszEvaluation: h = plot (obj)
plot (obj) plots the Calinski-Harabasz criterion values
against the number of clusters. The optimal number of clusters is marked
with an asterisk.
h = plot (obj) additionally returns the handle to the
plot axes.
See also: CalinskiHarabaszEvaluation, evalclusters
CalinskiHarabaszEvaluation: obj = compact (obj)
This method is not yet implemented for
CalinskiHarabaszEvaluation objects.
See also: CalinskiHarabaszEvaluation, evalclusters