# CalinskiHarabaszEvaluation

Calinski-Harabasz criterion clustering evaluation object

## Description

`CalinskiHarabaszEvaluation`

is an object consisting of sample data
(`X`

), clustering data (`OptimalY`

), and Calinski-Harabasz
criterion values (`CriterionValues`

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

). The Calinski-Harabasz
criterion is sometimes called the variance ratio criterion (VRC). Well-defined clusters have a
large between-cluster variance and a small within-cluster variance. The optimal number of
clusters corresponds to the solution with the highest Calinski-Harabasz index value. For more
information, see Calinski-Harabasz Criterion.

## Creation

Create a Calinski-Harabasz criterion clustering evaluation object by using the `evalclusters`

function and specifying the criterion as
`"CalinskiHarabasz"`

.

You can then use `compact`

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

, `OptimalY`

, and
`Missing`

.

## Properties

## Object Functions

## Examples

## More About

## References

[1] Calinski, T., and J. Harabasz.
“A dendrite method for cluster analysis.” *Communications in
Statistics*. Vol. 3, No. 1, 1974, pp. 1–27.

## Version History

**Introduced in R2013b**

## See Also

`evalclusters`

| `DaviesBouldinEvaluation`

| `GapEvaluation`

| `SilhouetteEvaluation`