Description Value References See Also

The structure and objects contained in MonoClust, an object returned from
the `MonoClust()`

function and used as the input in other functions in the
package.

- frame
Data frame in the form of a

`tibble::tibble()`

representing a tree structure with one row for each node. The columns include:- number
Index of the node. Depth of a node can be derived by

`number %/% 2`

.- var
Name of the variable used in the split at a node or

`"<leaf>"`

if it is a leaf node.- cut
Splitting value, so values of

`var`

that are smaller than that go to left branch while values greater than that go to the right branch.- n
Cluster size, the number of observations in that cluster.

- inertia
Inertia value of the cluster at that node.

- bipartsplitrow
Position of the next split row in the data set (that position will belong to left node (smaller)).

- bipartsplitcol
Position of the next split variable in the data set.

- inertiadel
Proportion of inertia value of the cluster at that node to the inertia of the root.

- medoid
Position of the data point regarded as the medoid of its cluster.

- loc
y-coordinate of the splitting node to facilitate showing on the tree. See

`plot.MonoClust()`

for details.- split.order
Order of the splits with root is 0.

- inertia_explained
Percent inertia explained as described in Chavent (2007). It is

`1 - (sum(current inertia)/inertial[1])`

.- alt
A nested tibble of alternate splits at a node. It contains

`bipartsplitrow`

and`bipartsplitcol`

with the same meaning above. Note that this is only for information purpose. Currently`monoClust`

does not support choosing an alternate splitting route. Running`MonoClust()`

with`nclusters = 2`

step-by-step can be run if needed.

- membership
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.- dist
Distance matrix calculated using the method indicated in

`distmethod`

argument of`MonoClust()`

.- terms
Vector of variable names in the data that were used to split.

- centroids
Data frame with one row for centroid value of each cluster.

- medoids
Named vector of positions of the data points regarded as medoids of clusters.

- alt
Indicator of having an alternate splitting route occurred when splitting.

- circularroot
List of values designed for circular variable in the data set.

`var`

is the name of circular variable and`cut`

is its first best split value. If circular variable is not available, both objects are NULL.

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.

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