# agglomerativeHC.details: To explain agglomerative hierarchical clusterization... In LearnClust: Learning Hierarchical Clustering Algorithms

## Description

To explain the complete agglomerative hierarchical clusterization algorithm choosing distance and approach type.

## Usage

 `1` ```agglomerativeHC.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 agglomerative hierarchical clusterization method. It explains the theoretical algorithm step by step.

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

2 - It creates the clusters.

3 - It calculates a matrix distance with the clusters created by applying the distance and the approach given.

4 - It chooses the distance value and gets the clusters.

5 - It groups the clusters in a new one and updates clusters list.

6 - It repeats these steps until an unique cluster exists.

## Value

agglomerative algorithm 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) agglomerativeHC.details(a,'EUC','MAX') agglomerativeHC.details(matrixA,'MAN','AVG') agglomerativeHC.details(dataFrameA,'CAN','MIN') ```