arlc_get_apriori_thresholds {arlclustering}R Documentation

Get Apriori Thresholds

Description

This function takes a transaction dataset and ranges for support and confidence, computes the best thresholds, and returns the best minimum support, minimum confidence, best lift, total number of gross rules, and ratio of generated rules to total number of transactions.

Usage

arlc_get_apriori_thresholds(trx, supportRange, Conf)

Arguments

trx

A transaction dataset of class transactions from the arules package.

supportRange

A sequence of values representing the range for minimum support.

Conf

A sequence of values representing the range for minimum confidence.

Details

This function generates gross rules based on the best obtained thresholds.

This function iterates through the given ranges of support and confidence values, applies the Apriori algorithm to find association rules for each pair of values, and selects the pair that produces rules with the highest lift. The function then returns the best thresholds along with the lift, number of rules, and their ratio to the total transactions.

Value

A list containing:

minSupp

The best minimum support value.

minConf

The best minimum confidence value.

bestLift

The highest lift value obtained.

lenRules

The total number of gross rules generated.

ratio

The ratio of generated rules to the total number of transactions.

Examples


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$graph)
supportRange <- seq(0.1, 0.2, by = 0.1)
Conf <- 0.5
params <- arlc_get_apriori_thresholds(trans, supportRange, Conf)
message(params$minSupp)
message(params$minConf)
message(params$bestLift)
message(params$lenRules)
message(params$ratio)



[Package arlclustering version 1.0.5 Index]