Perform Single-Linkage Agglomerative Clustering.

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

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)
library(MASS)
library(magrittr)
library(purrr)
#> 
#> Attaching package: ‘purrr’
#> The following object is masked from ‘package:magrittr’:
#> 
#>     set_names

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)
#> NULL