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#'
#' Method that runs the agnes algorithm using the Euclidean metric to
#' make an external or internal validation of the cluster.
#'
#' @param dt Matrix or data frame with the set of values to be applied to the
#' algorithm.
#'
#' @param clusters It's an integer that indexes the number of clusters we want to
#' create.
#'
#' @param metric It's a characters vector with the metrics avalaible in the
#' package. The metrics implemented are: Entropy, Variation_information,
#' Precision,Recall,F_measure,Fowlkes_mallows_index,Connectivity,Dunn,
#' Silhouette.
#'
#' @return Return a list with both the internal and external evaluation of the
#' grouping.
#'
#' @keywords internal
agnes_euclidean_method = function(dt, clusters, metric) {
start.time <- Sys.time()
if ('data.frame' %in% class(dt))
dt = as.matrix(dt)
numeric_cluster <- ifelse(!is.numeric(clusters),1,0)
if (sum(numeric_cluster)>0)
stop('The field clusters must be a numeric')
agnes_euclidean <- tryCatch({
agnes(
x = dt,
metric = CONST_EUCLIDEAN,
stand = FALSE,
trace.lev = CONST_ZERO
)
},
error = function(cond) {
return(CONST_NULL)
})
if (!is.null(agnes_euclidean)) {
ev_agnes_euclidean <-
tryCatch({
external_validation(agnes_euclidean$order,
cutree(agnes_euclidean, k = clusters),metric)
},
error = function(cond) {
ev_agnes_euclidean = initializeExternalValidation()
})
iv_agnes_euclidean <- tryCatch({
internal_validation(
distance = agnes_euclidean$diss,
clusters_vector = cutree(agnes_euclidean, k = clusters),
dataf = dt,
method = CONST_EUCLIDEAN,
metric
)
},
error = function(cond) {
iv_agnes_euclidean = initializeInternalValidation()
})
} else {
ev_agnes_euclidean = initializeExternalValidation()
iv_agnes_euclidean = initializeInternalValidation()
}
end.time <- Sys.time()
time <- end.time - start.time
ev_agnes_euclidean$time = time - iv_agnes_euclidean$time
iv_agnes_euclidean$time = time - ev_agnes_euclidean$time
result = list("external" = ev_agnes_euclidean,
"internal" = iv_agnes_euclidean)
return (result)
}
#' Method that runs the agnes algorithm using the manhattan metric to make an
#' external or internal validation of the cluster
#'
#' @param dt matrix or data frame with the set of values to be applied to the
#' algorithm.
#' @param clusters is an integer that indexes the number of clusters we want to
#' create.
#' @param metric is a characters vector with the metrics avalaible in the
#' package. The metrics implemented are: entropy, variation_information,
#' precision,recall,f_measure,fowlkes_mallows_index,connectivity,dunn,
#' silhouette.
#'
#' @return returns a list with both the internal and external evaluation of the
#' grouping.
#'
#' @keywords internal
#'
agnes_manhattan_method = function(dt, clusters, metric) {
start.time <- Sys.time()
if ('data.frame' %in% class(dt))
dt = as.matrix(dt)
numeric_cluster <- ifelse(!is.numeric(clusters),1,0)
if (sum(numeric_cluster)>0)
stop('The field clusters must be a numeric')
agnes_manhattan <- tryCatch({
agnes(
x = dt,
metric = CONST_MANHATTAN,
stand = FALSE,
trace.lev = CONST_ZERO
)
},
error = function(cond) {
return(CONST_NULL)
})
if (!is.null(agnes_manhattan)) {
ev_agnes_manhattan <-
tryCatch({
external_validation(agnes_manhattan$order,
cutree(agnes_manhattan, k = clusters),metric)
},
error = function(cond) {
ev_agnes_manhattan = initializeExternalValidation()
})
iv_agnes_manhattan <- tryCatch({
internal_validation(
distance = agnes_manhattan$diss,
clusters_vector = cutree(agnes_manhattan, k = clusters),
dataf = dt,
method = CONST_MANHATTAN,
metric
)
},
error = function(cond) {
iv_agnes_manhattan = initializeInternalValidation()
})
} else {
ev_agnes_manhattan = initializeExternalValidation()
iv_agnes_manhattan = initializeInternalValidation()
}
end.time <- Sys.time()
time <- end.time - start.time
ev_agnes_manhattan$time = time - iv_agnes_manhattan$time
iv_agnes_manhattan$time = time - ev_agnes_manhattan$time
result = list("external" = ev_agnes_manhattan,
"internal" = iv_agnes_manhattan)
return (result)
}
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