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Class Definition: DaviesBouldinEvaluation

statistics: DaviesBouldinEvaluation

Davies-Bouldin object to evaluate clustering solutions

A DaviesBouldinEvaluation object is a ClusterCriterion object used to evaluate clustering solutions using the Davies-Bouldin criterion.

The Davies-Bouldin criterion is based on the ratio between the distances between clusters and within clusters, that is between centroids and between each datapoint and its centroid.

The best solution according to the Davies-Bouldin criterion is the one that produces the lowest Davies-Bouldin value.

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

Source Code: DaviesBouldinEvaluation

The DaviesBouldinEvaluation class contains the following properties:

A character vector or a function handle specifying the clustering algorithm used to generate the clustering solutions. It can be empty if the clustering solutions are passed as an input matrix. This property is read-only.

A character vector specifying the name of the criterion used to evaluate the clustering solutions. This property is read-only.

A numeric vector containing the values generated by the evaluation criterion for each clustering solution. This property is read-only.

A numeric vector containing the list of the number of clusters evaluated. This property is read-only.

A logical vector indicating which observations in the data matrix contain missing values (NaN). This property is read-only.

An integer specifying the number of non-missing observations in the data matrix. This property is read-only.

An integer specifying the optimal number of clusters based on the evaluation criterion. This property is read-only.

A numeric vector representing the clustering solution that corresponds to the optimal number of clusters. This property is read-only.

A numeric matrix containing the data used for clustering. This property is read-only.

The DaviesBouldinEvaluation class offers the following public methods:

statistics: obj = DaviesBouldinEvaluation (x, clust, KList)

obj = DaviesBouldinEvaluation (x, clust, KList) clusters the data in x for every cluster count in KList and evaluates each solution. The evaluation runs at construction, so obj arrives with its CriterionValues and OptimalK already set.

  • x is an N×P numeric matrix of observations (rows) and predictors (columns). A row holding a NaN is left out of NumObservations.
  • clust names the clustering method, one of 'kmeans', 'linkage' and 'gmdistribution'; or a function handle that clusters the data; or an N×M numeric matrix of clustering solutions computed elsewhere, one column per cluster count, in which case ClusteringFunction is left empty.
  • KList is a vector of positive integers, the cluster counts to inspect.

evalclusters is the usual way to create one of these objects.

DaviesBouldinEvaluation: obj = addK (obj, K)

DaviesBouldinEvaluation: plot (obj)
DaviesBouldinEvaluation: h = plot (obj)

Plot the CriterionValues against InspectedK from the DaviesBouldinEvaluation ClusterCriterion to the current plot. Returns an axes handle if requested.