ARLClustering - Testing Karate dataset

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

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(arlclustering)
#library(igraph)

Dataset description

The Karate Club social network dataset is provided as a gml file, containing 34 nodes and 78 edges.

Loading network dataset

Loading network dataset

• Graph Label : Karate Club Network
• Total Nodes : 34
• Total Edges : 78
• Average Degree : 4.588235

# Start the timer
t1 <- system.time({

  dataset_path <- system.file("extdata", "karate.gml", package = "arlclustering")
  if (dataset_path == "") {
    stop("karate.gml file not found")
  }

  g <- arlc_get_network_dataset(dataset_path, "Karate Club")
  g$graphLabel
  g$totalNodes
  g$totalEdges
  g$averageDegree
})

# Display the total processing time
message("Graph loading Processing Time: ", t1["elapsed"], " seconds\n")

Generate Transactions

Next, we generate the transactions datasets from the graph g. The length of the filtred transactional dataset is 28.

# Start the timer
t2 <- system.time({

  transactions <- arlc_gen_transactions(g$graph)
  transactions
})

# Display the total processing time
message("Transaction dataset Processing Time: ", t2["elapsed"], " seconds\n")

Get Apriori Thresholds

We obtain the apriori thresholds for the generated transactions. The following are the thresholds for the apriori execution: - The Minimum Support : 0.1 - The Minimum Confidence : 0.5 - The Lift : 7 - The Gross Rules length : 66 - The selection Ratio : 2

Graph loading Processing Time: 0.014 seconds.

# Start the timer
t3 <- system.time({
  params <- arlc_get_apriori_thresholds(transactions,
                                        supportRange = seq(0.1, 0.2, by = 0.1),
                                        Conf = 0.5)

  params$minSupp
  params$minConf
  params$bestLift
  params$lenRules
  params$ratio
})

# Display the total processing time
message("Graph loading Processing Time: ", t3["elapsed"], " seconds\n")

Generate Gross Rules

We use the obtained parameters to generate gross rules.

# Start the timer
t4 <- system.time({

  minLenRules <- 1
  maxLenRules <- params$lenRules
  if (!is.finite(maxLenRules) || maxLenRules > 5*length(transactions)) {
    maxLenRules <- 5*length(transactions)
  }

  grossRules <- arlc_gen_gross_rules(transactions,
                                     minSupp = params$minSupp,
                                     minConf = params$minConf,
                                     minLenRules = minLenRules+1,
                                     maxLenRules = maxLenRules)
})
# Display the total number of clusters and the total processing time
message("Gross rules generation Time: ", t4["elapsed"], " seconds\n")

Filter Significant and Non-Redundant Rules

We filter out redundant rules from the generated gross rules. Next, we filter out non-significant rules from the non-redundant rules, and we obtain the 50 rule items.

t5 <- system.time({
  NonRedRules <- arlc_get_NonR_rules(grossRules$GrossRules)

  NonRSigRules <- arlc_get_significant_rules(transactions,
                                             NonRedRules$FiltredRules)
  NonRSigRules$TotFiltredRules
})
# Display the total number of clusters and the total processing time
message("Clearing rules Processing Time: ", t5["elapsed"], " seconds\n")

Clean and genarate final Rules

We clean the final set of rules to prepare for clustering. Then, we generate clusters based on the cleaned rules. The total identified clusters is 12 clusters.

t6 <- system.time({
  cleanedRules <- arlc_clean_final_rules(NonRSigRules$FiltredRules)
  clusters <- arlc_generate_clusters(cleanedRules)
  clusters$TotClusters
})
# Display the total number of clusters and the total processing time
message("Cleaning final rules Processing Time: ", t6["elapsed"], " seconds\n")

message("The total comsumed time is:",t1["elapsed"]+ t2["elapsed"]+t3["elapsed"]+t4["elapsed"]+t5["elapsed"]+t6["elapsed"], "seconds\n")

Plot Clusters

Finally, we visualize the identified clusters.

arlc_clusters_plot(g$graph,
                   g$graphLabel,
                   clusters$Clusters)

arlclustering documentation built on Sept. 11, 2024, 8:07 p.m.