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Louvain clustering with bin and unimodal merging

Source: R/cluster_louvain.R
cluster_louvain_merge.Rd

End-to-end wrapper that applies Louvain clustering (cluster_louvain()), then merges clusters by variable bin thresholds (cluster_merge_bin()), and finally merges unimodal neighbours (cluster_merge_unimodal()).

Usage

cluster_louvain_merge(
  data,
  vars = NULL,
  thresholds,
  k = 15L,
  resolution = 1,
  max_label_diff = 2L,
  ignore_labels = NULL,
  dip_threshold = 0.15,
  min_mode_dist = NULL,
  max_iterations = 50L
)

Arguments

data

matrix or data.frame. Rows are observations, columns are variables.

vars

character vector or NULL. Column names to use for clustering. If NULL (default), all columns are used.

thresholds

named list. Passed to cluster_merge_bin(). Each element is a numeric vector of threshold values for the variable given by the element name.

k

integer(1). Number of nearest neighbours used to build the kNN graph. Default 15L.

resolution

numeric(1). Resolution parameter passed to igraph::cluster_louvain(). Higher values yield more (smaller) clusters. Default 1.

max_label_diff

integer(1). Passed to cluster_merge_unimodal(). Default 2L.

ignore_labels

character vector or NULL. Passed to cluster_merge_unimodal(). Default NULL.

dip_threshold

numeric(1). Passed to cluster_merge_unimodal(). Default 0.15.

min_mode_dist

numeric or named numeric vector or NULL. Passed to cluster_merge_unimodal(). Default NULL.

max_iterations

integer(1). Passed to cluster_merge_unimodal(). Default 50L.

Value

The list returned by cluster_merge_unimodal(), with elements assign and label and a "thresholds" attribute.

Examples

set.seed(1)
mat <- matrix(
  c(rnorm(50, -3), rnorm(50, 3), rnorm(100, 0)),
  ncol = 2,
  dimnames = list(NULL, c("v1", "v2"))
)
result <- cluster_louvain_merge(
  mat,
  vars = c("v1", "v2"),
  thresholds = list(v1 = 0, v2 = 0),
  k = 10
)
table(result$assign$merged)
#> 
#> 1_1 2_1 
#>  50  50