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

## Description

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

## Usage

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

## Arguments

 `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'.

## Details

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.

## Value

Matrix distance. Explanation.

## Author(s)

Roberto Alcántara roberto.alcantara@edu.uah.es

 ```1 2 3 4 5 6 7 8 9``` ```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) ```