# SilhouetteEvaluation

Silhouette criterion clustering evaluation object

## Description

`SilhouetteEvaluation`

is an object consisting of sample data
(`X`

), clustering data (`OptimalY`

), and silhouette criterion
values (`CriterionValues`

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

). The silhouette value for
each point (observation in `X`

) is a measure of how similar that point is to
other points in the same cluster, compared to points in other clusters. If most points have a
high silhouette value, then the clustering solution is appropriate. If many points have a low
or negative silhouette value, then the clustering solution might have too many or too few
clusters. For more information, see Silhouette Value and Criterion.

## Creation

Create a silhouette criterion clustering evaluation object by using the `evalclusters`

function and specifying the criterion as
`"silhouette"`

.

You can then use `compact`

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

, `OptimalY`

, and
`Missing`

.

## Properties

## Object Functions

## Examples

## More About

## References

[1] Kaufman, L., and P. J. Rouseeuw.
*Finding Groups in Data: An Introduction to Cluster Analysis*. Hoboken,
NJ: John Wiley & Sons, Inc., 1990.

[2] Rouseeuw, P. J.
“Silhouettes: a graphical aid to the interpretation and validation of cluster
analysis.” *Journal of Computational and Applied Mathematics*.
Vol. 20, No. 1, 1987, pp. 53–65.

## Version History

**Introduced in R2013b**