### Function Reference: `silhouette`

statistics: silhouette (X, clust)
statistics: [si, h] = silhouette (X, clust)
statistics: [si, h] = silhouette (…, Metric, MetricArg)

Compute the silhouette values of clustered data and show them on a plot.

X is a n-by-p matrix of n data points in a p-dimensional space. Each datapoint is assigned to a cluster using clust, a vector of n elements, one cluster assignment for each data point.

Each silhouette value of si, a vector of size n, is a measure of the likelihood that a data point is accurately classified to the right cluster. Defining "a" as the mean distance between a point and the other points from its cluster, and "b" as the mean distance between that point and the points from other clusters, the silhouette value of the i-th point is:

$$ S_i = \frac{b_i - a_i}{max(a_1,b_i)} $$

Each element of si ranges from -1, minimum likelihood of a correct classification, to 1, maximum likelihood.

Optional input value Metric is the metric used to compute the distances between data points. Since `silhouette` uses `pdist` to compute these distances, Metric is quite similar to the option Metric of pdist and it can be:

- A known distance metric defined as a string: `Euclidean`, `sqEuclidean` (default), `cityblock`, `cosine`, `correlation`, `Hamming`, `Jaccard`.
- A vector as those created by `pdist`. In this case X does nothing.
- A function handle that is passed to `pdist` with MetricArg as optional inputs.

Optional return value h is a handle to the silhouette plot.

Reference Peter J. Rousseeuw, Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis. 1987. doi:10.1016/0377-0427(87)90125-7

See also: dendrogram, evalclusters, kmeans, linkage, pdist

Source Code: silhouette

### Example: 1

     load fisheriris;
     X = meas(:,3:4);
     cidcs = kmeans (X, 3, "Replicates", 5);
     silhouette (X, cidcs);
     y_labels(cidcs([1 51 101])) = unique (species);
     set (gca, "yticklabel", y_labels);
     title ("Fisher's iris data");