GapEvaluation
statistics: GapEvaluation
Gap evaluation for clustering solutions
The GapEvaluation class implements the gap statistic criterion for
evaluating clustering solutions. A GapEvaluation object is a
specialization of ClusterCriterion and contains fields and methods
to compute the gap statistic, its Monte-Carlo reference expectations, and
to select the optimal number of clusters according to a chosen search
method.
Create a GapEvaluation object by using the evalclusters
function or by calling the class constructor directly.
See also: evalclusters, ClusterCriterion, CalinskiHarabaszEvaluation, DaviesBouldinEvaluation, SilhouetteEvaluation
Source Code: GapEvaluation
A positive integer specifying how many reference datasets are generated to compute the expected log within-cluster dispersion via Monte-Carlo simulation. This property is read-only.
A character vector or function handle specifying the distance measure
passed to clustering routines (as accepted by pdist). When a
numeric vector is supplied it is interpreted as a precomputed distance
vector. This property is read-only.
A character vector naming the reference distribution used to generate
reference datasets. Supported values include 'pca' and
'uniform'. This property is read-only.
A character vector specifying the method used to select the optimal
number of clusters from the gap statistic. Supported values include
'globalMaxSE' and 'firstMaxSE'. This property is
read-only.
A numeric vector containing the Monte-Carlo estimate of the expected values for the natural logarithm of the within-cluster dispersion, computed across the generated reference datasets. This property is read-only.
A numeric vector containing the observed values of the natural logarithm of the within-cluster dispersion computed on the actual data. This property is read-only.
A numeric vector containing the standard error of the expected values for the natural logarithm of the within-cluster dispersion. This property is read-only.
A numeric vector containing the standard deviation of the Monte-Carlo estimates of the log within-cluster dispersion. This property is read-only.
GapEvaluation: obj = addK (obj, K)
Add a new cluster array to inspect in the GapEvaluation object.
This updates internal storage for Monte-Carlo results and evaluates the
newly requested cluster counts.
ClusterCriterion: plot (obj)
ClusterCriterion: h = plot (obj)
Plot the gap statistic (criterion values) versus the inspected numbers of clusters and display error bars representing the Monte-Carlo standard deviations. Optionally returns the axes handle.
GapEvaluation: obj = compact (obj)
Return a compact representation of the GapEvaluation object.
Currently not implemented; calling this method will issue a warning.