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

Description Usage Arguments Details Value Author(s) Examples

View source: R/agglomerativeHC.details.R

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

1	agglomerativeHC.details(data, distance, approach)

`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.

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.

agglomerative algorithm explanation.

Roberto Alcántara roberto.alcantara@edu.uah.es

Juan José Cuadrado jjcg@uah.es

Universidad de Alcalá de Henares

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')