clust_compute: Compute DBSCAN clusters from a umapscan object
In juba/umapscan: UMAP and DBSCAN Pragmatic and Supervised clustering

Description Usage Arguments Value See Also Examples

This function runs DBSCAN with the specified arguments to compute new clusters on a umapscan object. Only points which don't have an already validated cluster are taken into account.

clust_compute(
  us,
  parent = "",
  noise_only = FALSE,
  eps,
  minPts,
  graph = TRUE,
  alpha = 1,
  ellipses = TRUE
)

`us`	umapscan object
`parent`	name of the parent cluster
`noise_only`	only compute clusters for current 'Noise' points
`eps`	`eps` argument passed to `dbscan::dbscan()`
`minPts`	`minPts` argument passed to `dbscan::dbscan()`
`graph`	if TRUE, display a plot of the computed clusters
`alpha`	point transparency for clusters plot
`ellipses`	if TRUE, plot confidence ellipses around clusters

Returns an updated umapscan object, and optionally displays a clusters plot.

new_umapscan(), clust_describe(), clust_plot()

library(dplyr)
iris_num <- iris %>% select_if(is.numeric)
us <- new_umapscan(iris_num, n_neighbors = 25, min_dist = 0.1, seed = 1337)
us <- clust_compute(us, minPts = 3, eps = 0.5)
clust_compute(us, minPts = 3, eps = 0.45, alpha = 1, parent = "3")