# R/mdAgglomerative.details.R In LearnClust: Learning Hierarchical Clustering Algorithms

#### Documented in mdAgglomerative.details

```#' @title Matrix distance by distance and approach type.
#' @description To explain how to calculate the matrix distance by using \code{distance} and \code{approach} type.
#' @param list is a clusters list.
#' @param distance is a literal. The distance type to be used.
#' @param approach is a literal. The approach type to be used.
#' @details This function is part of the hierarchical clusterization method. The function calculates the
#' matrix distance by using the distance and approach type given.
#' @details The \code{list} parameter will be a list with the clusters as rows and columns.
#' @details The function avoids distances equal 0 and undefined clusters.
#' @author Roberto Alcántara \email{roberto.alcantara@@edu.uah.es}
#' @author Universidad de Alcalá de Henares
#' @return A matrix distance. Explanation.
#' @examples
#'
#' data <- c(1,2,1,3,1,4,1,5,1,6)
#'
#' clusters <- toList(data)
#'
#' mdAgglomerative.details(clusters, 'EUC', 'MAX')
#'
#' mdAgglomerative.details(clusters, 'CHE', 'AVG')
#'
#' @export

mdAgglomerative.details <- function(list,distance,approach){
message("\n 'mdAgglomerative' creates the matrix distance of every cluster given by 'list' parameter.\n")
message("\n It uses", distance, "distance and", approach, "approach.\n")
res <- c()
for (i in seq_len(length(list))){
for (j in seq_len(length(list))){
if(is.null(list[[i]]) | is.null(list[[j]])){
dist <- 0
} else if(i ==j){
dist <- 0
}
else {
dist <- clusterDistance(list[[i]],list[[j]],approach,distance)
}
res <- c(res, dist)
}
}
solution <- matrix(res, nrow=length(list))
message("\n It returns the matrix with all the distances between clusters depending on distance and approach. \n")
message("\n The matrix distance is: \n\n")
print(solution)
}
```

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LearnClust documentation built on Nov. 30, 2020, 1:09 a.m.