UAHDataScienceUC (version 1.0.1)

divisive_clustering: Divisive Hierarchical Clustering

Description

Perform a hierarchical Divisive cluster analysis on a set of observations

Usage

divisive_clustering(data, learn = FALSE, waiting = TRUE, ...)

Value

An stats::hclust() object which describes the tree produced by the clustering process.

Arguments

data

a set of observations, presented as a matrix-like object where every row is a new observation.

learn

a Boolean determining whether intermediate logs explaining how the algorithm works should be printed or not.

waiting

a Boolean determining whether the intermediate logs should be printed in chunks waiting for user input before printing the next or not.

...

additional arguments passed to clustlearn::kmeans().

Author

Eduardo Ruiz Sabajanes,

Details

This function performs a hierarchical cluster analysis for the \(n\) objects being clustered. The definition of a set of clusters using this method follows a \(n\) step process, which repeats until \(n\) clusters remain:

1. Initially, each object is assigned to the same cluster. The sum of squares of the distances between objects and their cluster center is computed.

2. The cluster with the highest sum of squares is split into two using the k-means algorithm. This step is repeated until \(n\) clusters remain.

Examples

### !! This algorithm is very slow, so we'll only test it on some datasets !!

### Helper function
test <- function(db, k) {
  # Save old par settings
  old_par <- par(no.readonly = TRUE)
  # Ensure par settings are restored when function exits
  on.exit(par(old_par))

  print(cl <- divisive_clustering(db, max_iterations = 5))
  par(mfrow = c(1, 2))
  plot(db, col = cutree(cl, k), asp = 1, pch = 20)
  h <- rev(cl$height)[50]
  clu <- as.hclust(cut(as.dendrogram(cl), h = h)$upper)
  ctr <- unique(cutree(cl, k)[cl$order])
  plot(clu, labels = FALSE, hang = -1, xlab = "Cluster", sub = "", main = "")
  rect.hclust(clu, k = k, border = ctr)
}

### Example 1
# test(db1, 2)

### Example 2
# test(db2, 2)

### Example 3
# test(db3, 3)

### Example 4
# test(db4, 3)

### Example 5
test(db5, 3)

### Example 6
test(db6, 3)

### Example 7 (with explanations, no plots)
  cl <- divisive_clustering(
  db5[1:6, ],
  learn = TRUE,
  waiting = FALSE
)