The other vignette focuses on reproducing a single clustering workflow that assumes that the number of clusters has been decided. As the app includes a few options for evaluating clusters, some of the functions are also made available in the package. The output of the clustering functions can also be used with other packages.

```
numeric_data <- iris %>% select(Sepal.Length, Sepal.Width, Petal.Width)
dmat <- compute_dmat(numeric_data, "euclidean", TRUE)
clusters <- compute_clusters(dmat, "complete")
```

For Gap statistic, the optimal number of clusters depends on the
method use to compare cluster solutions. The package cluster includes
the function `cluster::maxSE()`

to help with that.

```
gap_results <- compute_gapstat(scale(numeric_data), clusters)
optimal_k <- cluster::maxSE(gap_results$gap, gap_results$SE.sim)
line_plot(gap_results, "k", "gap", xintercept = optimal_k)
```

The Shiny app also includes the option to compute average silhouette
widths or Dunn index. The function `compute_metric`

works
similarly to `compute_gapstat`

, whereas
`optimal_score`

is similar to maxSE. However,
`optimal_score`

varies only between first and global minimum
and maximum.

```
res <- compute_metric(dmat, clusters, "dunn")
optimal_k <- optimal_score(res$score)
line_plot(res, "k", "score", optimal_k)
```