mdDivisive.details: Matrix distance by distance and approach type.
In LearnClust: Learning Hierarchical Clustering Algorithms

Description Usage Arguments Details Value Author(s) Examples

To explain how to calculate the matrix distance by using distance and approach types.

1	mdDivisive.details(list, distance, approach, components)

`list`	is a clusters list.
`distance`	is a string. The distance type to be used.
`approach`	is a string. The approach type to be used.
`components`	is a clusters list. It contains every clusters with only one element. It is used to check if complementary condition is 'TRUE'.

This function is part of the divisive hierarchical clusterization method. The function calculates the matrix distance by using the distance and approach types given.

The list parameter will be a list with the clusters as rows and columns.

The function avoids distances equal 0 and undefined clusters.

It also avoids distances between clusters that are not complementary because they can't be chosen to divide all the clusters.

Matrix distance. Explanation.

Roberto Alcántara roberto.alcantara@edu.uah.es

Juan José Cuadrado jjcg@uah.es

Universidad de Alcalá de Henares

data <- c(1,2,1,3,1,4,1,5,1,6)

clusters <- toList(data)

components <- toList(data)

mdDivisive.details(clusters, 'EUC', 'MAX', components)

mdDivisive.details(clusters, 'MAN', 'MIN', components)