R/arlc_generate_clusters.R

In arlclustering: Exploring Social Network Structures Through Friendship-Driven Community Detection with Association Rules Mining

#' Generate Clusters
#'
#' This function generates potential clusters based on preprocessed rules.
#'
#' @description This function takes a vector of preprocessed rules, combines elements
#' starting with the same value, groups segments by the starting value, sorts elements
#' within each segment, and returns potential clusters.
#'
#' @param vec A vector of preprocessed rules.
#'
#' @return A list of unique and potential clusters.
#'
#' @examples
#' \donttest{
#' library(arlclustering)
#' # Create a sample transactions dataset
#' sample_gml_file <- system.file("extdata", "karate.gml", package = "arlclustering")
#' g <- arlc_get_network_dataset(sample_gml_file, "Karate Club")
#' trans <- arlc_gen_transactions(g$graph)
#' supportRange <- seq(0.1, 0.2, by = 0.1)
#' Conf <- 0.5
#' params <- arlc_get_apriori_thresholds(trans, supportRange, Conf)
#' grossRules <- arlc_gen_gross_rules(trans, params$minSupp, params$minConf, 1, params$lenRules)
#' nonRR_rules <- arlc_get_NonR_rules(grossRules$GrossRules)
#' NonRRSig_rules <- arlc_get_significant_rules(trans, nonRR_rules$FiltredRules)
#' cleaned_rules <- arlc_clean_final_rules(NonRRSig_rules$FiltredRules)
#' clusters <- arlc_generate_clusters(cleaned_rules)
#' }
#' @importFrom graphics legend par
#' @importFrom grDevices rainbow
#' @importFrom igraph plot.igraph layout_with_fr
#' @export
#'

arlc_generate_clusters <- function(vec) {
  # Combine elements starting with the same value and remove duplicates
  combined_result <- lapply(vec, function(v) unique(unlist(v)))

  # Group segments by the starting value
  grouped_result <- split(combined_result, sapply(combined_result, `[`, 1))

  # Sort elements within each segment
  sorted_result <- lapply(grouped_result, function(v) sort(unique(unlist(v))))

  # Call external function to generate clusters
  clusters <- arlc_fct_clean_transactions(sorted_result)

  # Display obtained clusters
  # if (genFileLog) {
  #   message("Total Clusters: ", length(clusters))
  #   for (i in 1:length(clusters)) {
  #     message("\n  Set ", i, ":", paste(clusters[[i]], collapse = " "))
  #   }
  # }

  #return(clusters)
  return(list(
     TotClusters = length(clusters),
     Clusters = clusters
  ))
}