Semi-supervised hierarchical clustering — hclust

Semi-supervised hierarchical clustering by chosen groups with hclust.

Usage

hclust_semisupervised(
  data,
  groups,
  dist_method = "euclidean",
  dist_p = 2,
  hclust_method = "complete",
  cor_use = "everything",
  merge_height = NA
)

Arguments

data: a data.frame to be clustered by rows
groups: a list of vectors. If we unlist(groups), all elements must be present in the rownames of data. Each vector in the list will be treated as a separate group for the hierarchical clustering, and rejoined in order at the end.
dist_method: a distance computation method. Must be one of "euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski", "pearson", "spearman"
dist_p: the power of the Minkowski distance, if chosen dist_method is "minkowski"
hclust_method: an agglomeration method. Should be a method supported by hclust, one of: "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC).
cor_use: If using correlation as distance, chooses the method for computing covariances in the presence of missing values. See stats::cor.
merge_height: If provided, dendrogramws will be merged at that height.

Value

hclust_semisupervised returns a list. The first element of the list is the data, reordered so that the merged hclust object will work. The second element is the result of the semi-supervised hierarchical clustering.