R/getCluster.details.R

In LearnClust: Learning Hierarchical Clustering Algorithms

#' @title To explain how to get the clusters with minimal distance.
#' @description To explain how to get the clusters with the minimal distance value. By using the given \code{distance},
#' it gets the matrix index.
#' @param matrix is a numeric matrix.
#' @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 Numeric vector with two clusters indexs.
#' @examples
#'
#' matrixExample <- matrix(c(1:10), ncol=2)
#'
#' getCluster.details(2,matrixExample)
#'
#'
#' @export

getCluster.details <- function(distance,matrix){
   message("\n 'getCluster' method searches the clusters which have the distance given. \n")
   message(" Search for ", distance, " in: \n")
   print(matrix)
   found <- FALSE
   index <- 1
   while(!found & (index < length(matrix))){

     if(matrix[index] == distance){
       found <- TRUE
     } else {
       index <- index + 1
     }
   }
   cluster1 <- trunc(index/(nrow(matrix))) + 1
   cluster1 <- if((index %% (nrow(matrix))) == 0) (cluster1 - 1) else cluster1
   cluster2 <- index %% (nrow(matrix))
   cluster2 <- if(cluster2 == 0) (nrow(matrix)) else cluster2
   clusters <- c(cluster1,cluster2)
   message("\n The clusters with the minimum distance are: ", clusters[1], ", ", clusters[2], "\n")
   clusters
}