The node allows you to perform HCA using the following methods:
The first four go into the class of methods called Agglomerative. The last one is a Divisive method.
Agglomerative methods works as follows:
Divisive methods works as follows: Starting with one large cluster
containing all observations. Clusters are divided until each
cluster contains only a single observation. At each stage, the cluster
with the largest dissimilarity between any two of its observations is
selected. To divide the selected cluster, the algorithm first looks
for its most disparate observation, i.e. the observation which has the
largest average dissimilarity to the other observations of the
selected cluster. This observation initiates the ``splinter group''.
In subsequent steps, the algorithm reassigns observations that are
closer to the ``splinter group'' than to the ``old party''. The
result is a division of the selected cluster into two new
clusters.