cuda_ml_agglomerative_clustering: Perform single-linkage agglomerative clustering.

In cuda.ml: R Interface for the RAPIDS cuML Suite of Libraries

Perform single-linkage agglomerative clustering.

Description

Recursively merge the pair of clusters that minimally increases a given linkage distance.

Usage

cuda_ml_agglomerative_clustering(
  x,
  n_clusters = 2L,
  metric = c("euclidean", "l1", "l2", "manhattan", "cosine"),
  connectivity = c("pairwise", "knn"),
  n_neighbors = 15L
)

Arguments

x	The input matrix or data frame. Each data point should be a row and should consist of numeric values only.
n_clusters	The number of clusters to find. Default: 2L.
metric	Metric used for linkage computation. Must be one of {"euclidean", "l1", "l2", "manhattan", "cosine"}. If connectivity is "knn" then only "euclidean" is accepted. Default: "euclidean".
connectivity	The type of connectivity matrix to compute. Must be one of {"pairwise", "knn"}. Default: "pairwise". 'pairwise' will compute the entire fully-connected graph of pairwise distances between each set of points. This is the fastest to compute and can be very fast for smaller datasets but requires O(n^2) space. 'knn' will sparsify the fully-connected connectivity matrix to save memory and enable much larger inputs. "n_neighbors" will control the amount of memory used and the graph will be connected automatically in the event "n_neighbors" was not large enough to connect it.
n_neighbors	The number of neighbors to compute when connectivity is "knn". Default: 15L.

Value

A clustering object with the following attributes: "n_clusters": The number of clusters found by the algorithm. "children": The children of each non-leaf node. Values less than nrow(x) correspond to leaves of the tree which are the original samples. children[i + 1][1] and children[i + 1][2] were merged to form node (nrow(x) + i) in the i-th iteration. "labels": cluster label of each data point.

Examples

library(cuda.ml)
if (interactive() && cuda_ml_backend_info()$runtime_installed) {
  library(MASS)
  library(purrr)

  set.seed(0)

  gen_pts <- function() {
    centers <- list(c(1000, 1000), c(-1000, -1000), c(-1000, 1000))
    pts <- centers |>
      map(\(center) mvrnorm(50, mu = center, Sigma = diag(2)))

    do.call(rbind, pts)
  }

  clust <- cuda_ml_agglomerative_clustering(
    x = gen_pts(),
    metric = "euclidean",
    n_clusters = 3
  )

  print(clust$labels)
}

cuda.ml documentation built on Aug. 21, 2026, 9:14 a.m.