# DaviesBouldinEvaluation

Davies-Bouldin criterion clustering evaluation object

## Description

`DaviesBouldinEvaluation`

is an object consisting of sample data
(`X`

), clustering data (`OptimalY`

), and Davies-Bouldin
criterion values (`CriterionValues`

) used to
evaluate the optimal number of clusters (`OptimalK`

). The Davies-Bouldin
criterion is based on a ratio of within-cluster and between-cluster distances. The optimal
clustering solution has the smallest Davies-Bouldin index value. For more information, see
Davies-Bouldin Criterion.

## Creation

Create a Davies-Bouldin criterion clustering evaluation object by using the `evalclusters`

function and specifying the criterion as
`"DaviesBouldin"`

.

You can then use `compact`

to create a compact version of the
Davies-Bouldin criterion clustering evaluation object. The function removes the contents of
the properties `X`

, `OptimalY`

, and
`Missing`

.

## Properties

## Object Functions

## Examples

## More About

## References

[1] Davies, D. L., and D. W. Bouldin.
“A Cluster Separation Measure.” *IEEE Transactions on Pattern Analysis
and Machine Intelligence*. Vol. PAMI-1, No. 2, 1979, pp. 224–227.

## Version History

**Introduced in R2013b**