The structure and objects contained in PULS, an object returned from
the PULS() function and used as the input in other functions in the
package.
Data frame in the form of a tibble::tibble() representing
a tree structure with one row for each node. The columns include:
Index of the node. Depth of a node can be derived by
number %/% 2.
Name of the variable used in the split at a node or
"<leaf>" if it is a leaf node.
Cluster size, the number of observations in that cluster.
Weights of observations. Unusable. Saved for future use.
Inertia value of the cluster at that node.
Position of the next split row in the data set (that position will belong to left node (smaller)).
Position of the next split variable in the data set.
Proportion of inertia value of the cluster at that node to the inertia of the root.
Position of the data point regarded as the medoid of its cluster.
y-coordinate of the splitting node to facilitate showing
on the tree. See plot.PULS() for details.
Percent inertia explained as described in
Chavent (2007). It is 1 - (sum(current inertia)/inertial[1]).
Indicator of an alternative cut yielding the same reduction in inertia at that split.
Vector of the same length as the number of rows in the
data, containing the value of frame$number corresponding to the leaf
node that an observation falls into.
Distance matrix calculated using the method indicated in
distmethod argument of PULS().
Vector of subregion names in the data that were used to split.
Named vector of positions of the data points regarded as medoids of clusters.
Indicator of having an alternate splitting route occurred when splitting.
Chavent, M., Lechevallier, Y., & Briant, O. (2007). DIVCLUS-T: A monothetic divisive hierarchical clustering method. Computational Statistics & Data Analysis, 52(2), 687-701. doi: 10.1016/j.csda.2007.03.013 .