## Documentation Center |

**Package: **clustering.evaluation**Superclasses: **clustering.evaluation.ClusterCriterion

Silhouette criterion clustering evaluation object

`clustering.evaluation.SilhouetteEvaluation` is
an object consisting of sample data, clustering data, and silhouette
criterion values used to evaluate the optimal number of data clusters.
Create a silhouette criterion clustering evaluation object using `evalclusters`.

` eva = evalclusters(x,clust,'Silhouette')` creates
a silhouette criterion clustering evaluation object.

` eva = evalclusters(x,clust,'Silhouette',Name,Value)` creates
a silhouette criterion clustering evaluation object using additional
options specified by one or more name-value pair arguments.

addK | Evaluate additional numbers of clusters |

compact | Compact clustering evaluation object |

plot | Plot clustering evaluation object criterion values |

The silhouette value for each point is a measure of how similar
that point is to points in its own cluster, when compared to points
in other clusters. The silhouette value for the `i`th
point, `Si`, is defined as

Si = (bi-ai)/ max(ai,bi)

where `ai` is the average distance from the `i`th
point to the other points in the same cluster as `i`,
and `bi` is the minimum average distance from the `i`th
point to points in a different cluster, minimized over clusters.

The silhouette value ranges from -1 to +1.
A high silhouette value indicates that `i` is well-matched
to its own cluster, and poorly-matched to neighboring 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 may have either too many or too few clusters.
The silhouette clustering evaluation criterion can be used with any
distance metric.

[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.

`clustering.evaluation.CalinskiHarabaszEvaluation` | `clustering.evaluation.DaviesBouldinEvaluation` | `clustering.evaluation.GapEvaluation` | `evalclusters` | `silhouette`

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