R/arlc_get_NonR_rules.R

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

#' Get Non-Redundant Rules
#'
#' This function cleans the gross rules and provides non-redundant rules.
#'
#' @description This function takes a set of gross rules, removes redundant rules,
#' and returns the total number of non-redundant rules along with the non-redundant rules.
#'
#' @param gross_rules A vector or dataframe of gross rules.
#'
#' @return A list containing the total number of non-redundant rules and the non-redundant rules.
#'
#' @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)
#' }
#' @importFrom arules is.redundant
#' @export

arlc_get_NonR_rules <- function(gross_rules) {
  ## Cleaning rules
  # Remove redundant rules
  nonRR_rules <- gross_rules[!is.redundant(gross_rules)]
  if (length(nonRR_rules) < 1) {
    message('Non-redundant rule set is the same as the gross rule set...')
    nonRR_rules <- gross_rules
  }
  # Compute number of non-redundant rules
  total_nonRedandunt_rules <- length(nonRR_rules)

  # Return result as a list
  return(list(TotFiltredRules = total_nonRedandunt_rules,
              FiltredRules = nonRR_rules))
}