Nothing
#' @title To get the clusters with maximal distance.
#' @description To get the clusters with the maximal distance value. By using the given \code{distance},
#' it gets the matrix index.
#' @param vector is a numeric vector
#' @param distance is a number. It should be in the matrix.
#' @details This function is part of the hierarchical clusterization method. The function uses the
#' \code{distance} value and gets the clustersId with the minimal distance.
#' @details For the divisive algorithm, it chooses the distances from a distances list.
#' @author Roberto Alcántara \email{roberto.alcantara@@edu.uah.es}
#' @author Juan José Cuadrado \email{jjcg@@uah.es}
#' @author Universidad de Alcalá de Henares
#' @return A cluster.
#' @examples
#'
#' getClusterDivisive(2,c(1:10))
#'
#' getClusterDivisive(6,c(2,4,6,8,10,12))
#'
#' @export
getClusterDivisive <- function(distance,vector){
found <- FALSE
index <- 1
while(!found & (index < length(vector))){
if(vector[index] == distance){
found <- TRUE
} else {
index <- index + 1
}
}
cluster <- index %% (length(vector))
cluster <- if(cluster == 0) (length(vector)) else cluster
cluster
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.