cuml_agglomerative_clustering: Perform Single-Linkage Agglomerative Clustering.
In cuml: R Interface for the RAPIDS cuML Suite of Libraries

Description Usage Arguments Value Examples

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

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

`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.

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.

library(cuml)
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 = matrix(c(1, 0, 0, 1), nrow = 2)))

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

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

print(clust$labels)