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

Description Usage Arguments Details Value Author(s) Examples

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

1	divisiveHC.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 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.

A list with the divided clusters. 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)

divisiveHC.details(a,'EUC','MAX')

divisiveHC.details(matrixA,'MAN','AVG')

divisiveHC.details(dataFrameA,'CHE','MIN')