# divisiveHC.details: To explain the divisive hierarchical clusterization algorithm... In LearnClust: Learning Hierarchical Clustering Algorithms

## Description

To explain the complete divisive hierarchical clusterization algorithm by choosing distance and approach types.

## Usage

 `1` ```divisiveHC.details(data, distance, approach) ```

## Arguments

 `data` could be a numeric vector, a matrix or a numeric data frame. It will be transformed into matrix and list to be used. `distance` is a string. It chooses the distance to use. `approach` is a string. It chooses the approach to use.

## Details

This function is the main part of the divisive hierarchical clusterization method. It explains the theoretical algorithm step by step.

1 - The function transforms data in useful object to be used.

2 - It creates a cluster that includes every simple elements.

3 - It initializes posible clusters using the initial elements.

4 - It calculates a matrix distance with the clusters created in the 3rd step.

5 - It chooses the maximal distance value and gets the clusters to be divided.

6 - It divides the cluster into two new complementary clusters and updates the clusters list.

6 - It repeats these steps until every cluster can't be divided again. The solution includes every simple cluster.

## Value

A list with the divided clusters. Explanation

## Author(s)

Roberto Alcántara roberto.alcantara@edu.uah.es

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```a <- c(1,2,1,3,1,4,1,5,1,6) matrixA <- matrix(a,ncol=2) dataFrameA <- data.frame(matrixA) divisiveHC.details(a,'EUC','MAX') divisiveHC.details(matrixA,'MAN','AVG') divisiveHC.details(dataFrameA,'CHE','MIN') ```