Statistics: DaviesBouldinEvaluation

### Class Definition: `DaviesBouldinEvaluation`

Function File: obj = evalclusters (x, clust, `DaviesBouldin`)
Function File: obj = evalclusters (…, `Name`, `Value`)

A 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 the distances between the centroids and the distances between each datapoint and its centroid.

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

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

Source Code: DaviesBouldinEvaluation

### Method: `addK`

DaviesBouldinEvaluation: obj = addK (obj, K)

Add a new cluster array to inspect the DaviesBouldinEvaluation object.

### Method: `compact`

DaviesBouldinEvaluation: obj = compact (obj)

Return a compact DaviesBouldinEvaluation object (not implemented yet).

### Method: `plot`

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

Plot the evaluation results.

Plot the CriterionValues against InspectedK from the DaviesBouldinEvaluation ClusterCriterion, obj, to the current plot. It can also return a handle to the current plot.