| agglomerativeHC {LearnClust} | R Documentation |
To execute agglomerative hierarchical clusterization algorithm by distance and approach.
Description
To execute complete agglomerative hierarchical clusterization algorithm choosing distance and approach type.
Usage
agglomerativeHC(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 executes the theoretical algorithm step by step.
1 - The function transforms data in useful object to be used.
2 - It creates the clusters.
3 - It calculates a matrix distances with the clusters created applying distance and 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
R object with a dendrogram, the grouped clusters and the list with every cluster.
Author(s)
Roberto Alcántara roberto.alcantara@edu.uah.es
Juan José Cuadrado jjcg@uah.es
Universidad de Alcalá de Henares
Examples
a <- c(1,2,1,3,1,4,1,5,1,6)
matrixA <- matrix(a,ncol=2)
dataFrameA <- data.frame(matrixA)
agglomerativeHC(a,'EUC','MAX')
agglomerativeHC(matrixA,'MAN','AVG')
agglomerativeHC(dataFrameA,'CAN','MIN')