Perform single-linkage agglomerative clustering.

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 dataframe. 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(magrittr)
  library(purrr)

  set.seed(0L)

  gen_pts <- function() {
    centers <- list(c(1000, 1000), c(-1000, -1000), c(-1000, 1000))
    pts <- centers %>%
      map(~ mvrnorm(50, mu = .x, Sigma = diag(2)))

    rlang::exec(rbind, !!!pts) %>% as.matrix()
  }

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

  print(clust$labels)
}