Run the DBSCAN clustering algorithm.

Run the DBSCAN (Density-based spatial clustering of applications with noise) clustering algorithm.

Usage

cuda_ml_dbscan(x, min_pts, eps)

Arguments

x

The input matrix or data frame. Each data point should be a row and should consist of numeric values only.

min_pts, eps

A point p is a core point if at least min_pts are within distance eps from it.

Value

A list containing the cluster assignments of all data points. A data point not belonging to any cluster (i.e., "noise") will have NA as its cluster assignment.

Examples

library(cuda.ml)
if (interactive() && cuda_ml_backend_info()$runtime_installed) {
  gen_pts <- function() {
    centroids <- list(c(1000, 1000), c(-1000, -1000), c(-1000, 1000))

    pts <- centroids |>
      purrr::map(\(centroid) {
        MASS::mvrnorm(10, mu = centroid, Sigma = diag(2))
      })

    do.call(rbind, pts)
  }

  m <- gen_pts()
  clusters <- cuda_ml_dbscan(m, min_pts = 5, eps = 3)

  print(clusters)
}