Type:	Package
Title:	Divisive Hierarchical Clustering
Version:	0.1.0
Maintainer:	Shaun Wilkinson <shaunpwilkinson@gmail.com>
Description:	Contains a single function dclust() for divisive hierarchical clustering based on recursive k-means partitioning (k = 2). Useful for clustering large datasets where computation of a n x n distance matrix is not feasible (e.g. n > 10,000 records). For further information see Steinbach, Karypis and Kumar (2000) http://glaros.dtc.umn.edu/gkhome/fetch/papers/docclusterKDDTMW00.pdf.
License:	GPL-3
Encoding:	UTF-8
LazyData:	true
URL:	http://github.com/shaunpwilkinson/dclust
BugReports:	http://github.com/shaunpwilkinson/dclust/issues
Imports:	openssl, phylogram
RoxygenNote:	6.1.1
NeedsCompilation:	no
Packaged:	2019-09-05 03:57:20 UTC; shaun
Author:	Shaun Wilkinson [aut, cre], Paolo Giordani [aut]
Repository:	CRAN
Date/Publication:	2019-09-05 12:00:02 UTC

Divisive/bisecting heirarchcal clustering

Description

This function recursively splits an n x p matrix into smaller and smaller subsets, returning a "dendrogram" object.

Usage

dclust(x, method = "kmeans", stand = FALSE, ...)

Arguments

Details

This function creates a dendrogram by successively splitting the dataset into smaller and smaller subsets (recursive partitioning). This is a divisive, or "top-down" approach to tree-building, as opposed to agglomerative "bottom-up" methods such as neighbor joining and UPGMA. It is particularly useful for large large datasets with many records (n > 10,000) since the need to compute a large n * n distance matrix is circumvented.

If a more accurate tree is required, users can increase the value of nstart passed to kmeans via the ... argument. While this can increase computation time, it can improve accuracy considerably.

Value

Returns an object of class "dendrogram".

Author(s)

Shaun Wilkinson

References

Steinbach M, Karypis G, Kumar V (2000). A Comparison of Document Clustering Techniques. Proceedings of World Text Mining Conference, KDD2000, Boston.

Examples

## Cluster a subsample of the iris dataset
suppressWarnings(RNGversion("3.5.0"))
set.seed(999)
iris50 <- iris[sample(x = 1:150, size = 50, replace = FALSE),]
x <- as.matrix(iris50[, 1:4])
rownames(x) <- iris50[, 5]
dnd <- dclust(x, nstart = 20)
plot(dnd, horiz = TRUE, yaxt = "n")

## Color labels according to species
rectify_labels <- function(node, x){
  newlab <- factor(rownames(x))[unlist(node, use.names = FALSE)]
  attr(node, "label") <- newlab
  return(node)
}
dnd <- dendrapply(dnd, rectify_labels, x = x)

## Create a color palette as a data.frame with one row for each species
uniqspp <- as.character(unique(iris50$Species))
colormap <- data.frame(Species = uniqspp, color = rainbow(n = length(uniqspp)))
colormap[, 2] <- c("red", "blue", "green")

## Color the inner dendrogram edges
color_dendro <- function(node, colormap){
  if(is.leaf(node)){
    nodecol <- colormap$color[match(attr(node, "label"), colormap$Species)]
    attr(node, "nodePar") <- list(pch = NA, lab.col = nodecol)
    attr(node, "edgePar") <- list(col = nodecol)
  }else{
    spp <- attr(node, "label")
    dominantspp <- levels(spp)[which.max(tabulate(spp))]
    edgecol <- colormap$color[match(dominantspp, colormap$Species)]
    attr(node, "edgePar") <- list(col = edgecol)
  }
  return(node)
}
dnd <- dendrapply(dnd, color_dendro, colormap = colormap)

## Plot the dendrogram
plot(dnd, horiz = TRUE, yaxt = "n")